Perceptual & Motor Skills: Motor Skills & Ergonomics 2014, 119, 2, 441-454. © Perceptual & Motor Skills 2014
EFFECT OF NOISE INTENSITY AND ILLUMINATION INTENSITY ON VISUAL PERFORMANCE1, 2 CHIN-CHIUAN LIN Department of Business Administration, Kun-Shan University Summary.—The results of Experiment 1 indicated that noise and illumination intensity have a significant effect on character identification performance, which was better at 30 dBA than at 60 and 90 dBA, and better at 500 and 800 lux than at 200 lux. However, the interaction of noise and illumination intensity did not significantly affect visual performance. The results of Experiment 2 indicated that noise and illumination intensity also had a significant effect on reading comprehension performance, which was better at 30 dBA than at 60 and 90 dBA, and better at 500 lux than at 200 and 800 lux. Furthermore, reading comprehension performance was better at 500 lux lighting and 30 dBA noise than with 800 lux and 90 dBA. High noise intensity impaired visual performance, and visual performance at normal illumination intensity was better than at other illumination intensities. The interaction of noise and illumination had a significant effect on reading comprehension. These results indicate that noise intensity lower than 30 dBA and illumination intensity approximately 500 lux might be the optimal conditions for visual work.
Over the past decade, thin film transistor liquid-crystal displays (TFTLCDs) have become popular for information displays because of the rapid decrease in their price, their lower power consumption, their improved optical characteristics, and the increased size of displays. Generally, work in an office environment is affected by noise intensity and illumination intensity. Noise may negatively affect workers using TFT-LCDs; many studies have shown that noise can affect task performance by adding to the overall mental workload (Parsons, 2000). Studies have indicated that noise has a significant effect on the human reactions, such as sleep disturbance, activity disturbance, and uses of the environment (Laszlo, McRobie, Stansfeld, & Hansell, 2012). Effect of Noise on Work Hockey and Hamilton (1983) summarized the effect of noise on different components of performance, and found that the accuracy of responses was degraded. Furthermore, noise was also found to have a detrimental effect on short-term (working) memory function. Hancock, Conway, Address correspondence to Chin-Chiuan Lin, Department of Business Administration, KunShan University, 195, Kun-Da Road, Yung-Kang District, Tainan City, 710, Taiwan (R.O.C.) or e-mail ([email protected]). 2 This study was supported by a research grant from the National Science Council of the Republic of China, Grant No. NSC 101-2221-E-168-036. 1
DOI 10.2466/26.24.PMS.119c20z1
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ISSN 0031-5125
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Szalma, Ross, and Saxton (2005) indicated that noise had a moderate but deleterious effect on performance, though it had differential effects on various tasks. Jahncke, Hygge, Halin, Green, and Dimberg (2011) studied the cognitive and emotional effect of noisy conditions during work, using a simulated open-plan office. The results showed that the participants remembered fewer words and rated themselves less motivated to work with a high noise level than with a low level. Trimmel, Atzlsdorfer, Tupy, and Trimmel (2012) studied the effect of low intensity noise on cognitive learning and electrophysiological stress responses. Their data demonstrated a significant effect on cognition and physiology of low intensity background noise. The results showed an impairment of reproduction (cognition) and increased heart rate in noisy conditions. Clark, Head, and Stansfeld (2013) showed that exposure to noise can impair reading comprehension and increase noise annoyance, but does not affect the psychological health of children. Noise can affect not only psychological responses, but also affect physiological responses of humans. Morrison, Haas, Shaffner, Garrett, and Fackler (2003) studied the correlation between noise and stress in nurses and concluded that noise is a potentially significant contributor to elevated heart rate and tachycardia. Ryherd, Waye, and Ljungkvist (2008) used a questionnaire to examine nurses' perceptions of noisy environments. They found that 66% of nurses experienced irritation and fatigue, 43% had concentration problems, and 40% experienced tension headaches. Chang, Liu, Hsieh, Bao, and Lai (2012) found that over 24 hours, a 1-dBA increment in environmental noise was statistically associated with a sustained 1.25% mL/mmHg increase in arterial compliance, but a 2.12 kdyne·sec/cm3 decrease in arterial resistance. They concluded that environmental noise exposure can have both transient and sustained effects on adults' vascular condition. Chang, Hwang, Liu, Chen, and Wang (2013) indicated that prolonged exposure to noise levels above 85 dBA may increase males' systolic and diastolic blood pressure. Hancock, et al. (2005) indicated that noise degraded performance in two taxons, information processing and problem solving, both related to cognition. They also indicated that a vast amount of effort has been expended on the investigation of the effects of noise on non-auditory aspects of performance; information processing, attention, and memory typically can be affected to some extent. Conversely, Helton, Matthews, and Warm (2009) showed that vigilance in noisy conditions was significantly better than in quiet conditions. They also found that the presence of noise increased self-reported task engagement and improved performance. Furthermore, in his review, Parsons (2000) concluded that the effect of noise on non-auditory task performance was inconclusive, as different studies
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have indicated that noise reduces task performance, has no effect on task performance, or increases task performance. However, these inconsistent results for the effect of noise on workers' performance might be due to the different type of tasks (vigilance or cognitive) evaluated. Despite the inconsistent results for the effect of noise on performance, no study has found any quantifiable relationship between noise intensity and work performance (Hancock, et al., 2005). Higher noise intensity might reduce cognitive resources, thereby reducing visual performance. The effect of noise intensity on visual performance remains a topic of study. Effect of Illumination on Work Visual performance has many aspects (e.g., cognition, identification, and detection) and is used in varied tasks such as reading, proofreading, vigilance, and searching. Regardless of the indicators, according to the model of human information processing (Wickens, 1992), visual tasks can be divided into two categories: short-term (perception tasks which need response within a short time) and long-term (decision and selection tasks with relatively long processing times). Nearly all jobs require effectiveness and efficiency at both these types of tasks. Illumination intensity significantly affects human responses such as visual performance (Lin, 2005; Lee, Ko, Shen, & Chao, 2012), color discrimination (Yoshida & Yamamoto, 2002; Tseng, Chao, Feng, & Hwang, 2010), and visual workload (Lin, Feng, Chao, & Tseng, 2008). When a TFTLCD display is placed in a lighted environment, surface reflections are produced. Although the surface of a TFT-LCD screen has an antiglare polarizer (Choi & Miyasaka, 1993), it still reflects the lighting (Kubo, Uchi, Narutaki, Shinomiya, & Ishii, 2000) in proportion to the intensity of the lighting. Therefore, illumination intensity is an important consideration in workplace design.3 The illumination conditions for information-gathering work can also vary greatly. Hypothetically, either too low or too high illumination intensity might reduce visual performance. In short, there might be an optimal illumination intensity for visual work. Therefore, there is a need to further examine the effect of illumination intensity on visual performance. Pawlak (1986) proposed that the reflective property of a screen could be expressed as a reflection factor (q). The reflected illumination (RI) is equal to the reflection factor (q) multiplied by the illumination intensity (II). The luminance of reflected light (RL) is equal to the reflected illumination (RI) divided by π (RL = RI/π) (Eperjesi, Fowler, & Kempster, 1995). Therefore, the luminance of reflected light (RL) is equal to the reflection factor, which is obtained through multiplying the illumination by the reflected illumination intensity (RL = q*II/π). When the level of illumination intensity increases, the reflected light increases proportionally (Chung & Lu, 2003). 3
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Noise/Illumination Interaction Bhattacharya, Tripathi, and Kashyap (1989) found that the interaction of noise and reflected illumination has a significant effect on accuracy scores for a letter-cancellation test and a hand-precision test and on efficiency scores for the latter test. Takahashi, Sasaki, Saito, Hosokawa, Kurasaki, and Saito (2001) reported that a high luminance with noise had the most effect on subjective fatigue and mental activities in visual work. Liebl, Haller, Jödicke, Baumgartner, Schlittmeier, and Hellbrück (2012) found that even a low level of intelligible background speech significantly impaired short-term memory, reasoning ability, and well-being. In addition, the interaction of background speech and lighting conditions was found to affect perceived performance. However, no effect on cognitive performance was found. Because studies on the effect of the interaction between noise intensity and illumination intensity on visual work have not quantified the effect of the interaction between noise intensity and illumination intensity on visual performance, more work is needed. Research goal. To specify the effects of noise intensity and illumination intensity on visual performance (character identification and reading comprehension). Hypothesis 1. Higher noise intensity and either too low or too high illumination intensity will reduce visual performance. Experiment 1 SHORT-TERM VISUAL PERFORMANCE METHOD Participants Twenty (10 women, 10 men) students from Kun-Shan University were enrolled as participants (age range = 19–23 yr.). All had at least 0.8 corrected visual acuity or better and normal vision. Procedure This study evaluated two independent variables: noise intensity and illumination intensity. The experiment used three levels of noise intensity: 30 dBA (below the level used by Jahncke, et al., 2011), 60 dBA, and 90 dBA (the permissible exposure level at the workplace in Taiwan; Melamed & Bruhis, 1996). The noise used here was a recording from a bolt factory in Tainan, which was repeatedly (1 hr. cycles) broadcast by loudspeakers (1000 W), with the speakers located about 1.5 m behind the participants. Three levels of illumination intensity were used: 200 lux (low-level office illumination; Helander & Rupp, 1984), 500 lux (normal office of illumination; ANSI/IES, 1983), and 800 lux (high-level office illumination; Lin &
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Huang, 2006). Gray was used for the text and the background color, in order to prevent chromatic aberration (Charman, 1991). The luminance of the background was 40 cd/m2 and the luminance of the text was 5 cd/m2. Therefore, the luminance contrast was 8:1 (Lin, 2005) and the polarity was positive (Buchner & Baumgartner, 2007). All of the participants completed nine within-subjects experimental treatment combinations (3 noise intensity × 3 illumination intensity) for Experiment 1. Apparatus and Workplace Conditions A 17 in., CMV 745A TFT-LCD with a 433 mm diagonal screen provided an active viewing area of 338 mm horizontally and 272 mm vertically. The pixel resolution was 1024 horizontally and 768 vertically, and the center-to-center pixel spacing was approximately 0.35 mm. The screen images were refreshed at a rate of 72 Hz. The maximal luminance contrast ratio value and the maximal luminance of the TFT-LCD were approximately 150 and 210 cd/m2, respectively. The screen surface was coated with a SiO2 polarizer to reduce glare and reflection. The fluorescent lamps used were 40W FL40D/38, purchased from Taiwan Light Co. (Taiwan). The CIE (International Commission on Illumination; Commission internationale de l′éclairage) values for the TFT-LCD screen were measured using a Laiko Color Analyzer, DT-100 purchased from Laiko Co., Ltd. (Taiwan). The illumination intensity was measured using a TES-1330 digital lux meter purchased from TES Electronic Co. (Taiwan), and the noise intensity was measured using a NM102 noise meter purchased from NoiseMeters Co., Ltd. (UK). A Topcon SS-3 Screenscope was used to test the visual acuity of the participants. The experiment was held in a soundproof and light-obstructed room. The TFT-LCD was positioned on a table 70 cm in height (Horikawa, 2001). The inclination angle of the TFT-LCD screen was 105º (Turville, Psihogios, Uimer, & Mirka, 1998; Burgess-Limerick, Mon-Williams, & Coppard, 2000) with respect to the vertical axis. A headrest restrained each participant's head at 25 cm above the table and ensured a constant viewing distance of 55 cm during the experiment. There was no glare on the TFT-LCD screen. Task and Procedure The participants completed a character identification task. At the beginning of each trial a warning tone was sounded, to instruct the subject to visually fixate on an “X” at the center of the screen. A few seconds later (a uniform distribution from 2 to 6 sec.), a stimulus that was composed of four (Tadahiko, 1992) English capital letters was presented in the center area of the screen for 200 msec. (approximately one eye-fixation duration). The participants were required to write as many letters as they could
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identify, in their corresponding position, within 10 sec. The four English capital letters were presented at the corners of an area at the center of the screen that measured approximately 20 mm × 25 mm. The height of the 12-point letters was approximately 4.2 mm. The subtended visual angle of the letters was approximately 25 minutes of arc. One capital English letter (A-Z) occupied each position. Each letter was presented once in each position. There were 26 trials for each treatment. For the first trial, the computer randomly selected one English capital letter from the letter set for each of the positions: 1, 2, 3, and 4, respectively. These letters were then deleted from the letter set for that position and recorded in a file. In the second trial, the computer again randomly selected one letter from the remaining letter sets for positions 1, 2, 3, and 4. The procedure was repeated iteratively, until the letter sets were empty. In order to familiarize the participants with the character identification task, they were required to perform five training trials for each treatment. Each treatment required approximately 4 min. There was a 1 to 2 min. break between treatments, to avoid successive contrast effects and visual fatigue. The overall experiment lasted for approximately 1 hr. for each participant, including regular breaks to reduce fatigue. For each participant, the two within-subjects factor treatments were administered randomly. Before the experiment, the treatment sequence for each participant was determined by drawing lots. In order to maintain motivation, the participant were paid NT$150 per hour, plus an extra NT$150 if their overall average percentage of correctly identified letters exceeded 60%. Performance Measures and Data Analysis The percentage of correctly identified letters was used as the measure of short-term visual performance. The correctness was determined by the recalled position sequence. For example, if the presented position sequence for a particular trial was “BMSQ,” then the response “MSQB” was deemed to be incorrect. Previous studies have found that a range of illumination intensity between 50 and 3,000 lux does not affect a change in either physiological measures or in self-reported fatigue (Buchner & Baumgartner, 2007; Lin, Lin, Hwang, Jeng, & Liao, 2008). Therefore, this study did not measure the physiological responses or self-reported fatigue. Analysis of variance (ANOVA) was conducted, using the Statistical Analysis System (SAS 9.0). Sex was treated as a blocking effect (Montgomery & Montgomery, 1997) in the analysis. RESULTS The character identification performance values for each level of independent variables are shown in Table 1. The interaction effect of noise in-
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VISUAL PERFORMANCE TABLE 1 CHARACTER IDENTIFICATION AND READING COMPREHENSION PERFORMANCE FOR EACH NOISE AND ILLUMINATION INTENSITY AND DUNCAN GROUPING Independent Variable
n
Mean
SD
Duncan Grouping
Experiment 1 (Character identification) Sex Female
90
73.87
6.50
A
Male
90
68.33
4.71
B
30 dBA
60
75.78
5.70
A
60 dBA
60
70.72
5.20
B
90 dBA
60
66.80
4.44
C
500 lux
60
73.00
6.02
A
800 lux
60
71.92
5.74
A
200 lux
60
68.38
6.28
B
Noise intensity
Illumination intensity
Experiment 2 (Reading comprehension) Sex Female
90
6.61
0.83
A
Male
90
6.32
0.93
B
30 dBA
60
7.07
0.71
A
60 dBA
60
6.48
0.75
B
90 dBA
60
5.85
0.78
C
500 lux
60
6.85
0.76
A
200 lux
60
6.38
0.88
B
800 lux
60
6.17
0.91
B
Noise intensity
Illumination intensity
tensity and illumination intensity on character identification performance was not statistically significant (Table 2). The results in Table 2 also indicate that the main effects for noise intensity (F2, 170 = 81.43, p < .0001) and illumination intensity (F2, 170 = 23.40, p < .0001) were statistically significant. The Duncan multiple paired-comparisons (Table 1) show that a noise level of 30 dBA (75.78%) resulted in the best character identification performance, followed by 60 dBA (70.72%) and 90 dBA (66.80%). In terms of illumination intensity, 500 lux (73.0%) and 800 lux (71.92%) resulted in better character identification performance than 200 lux (68.38%). Overall, 30 dBA and 500 lux resulted in the best character identification performance (78.15%) and 90 dBA and 200 lux resulted in the worst performance (63.95%).
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C-C. LIN TABLE 2 ANOVA FOR CHARACTER IDENTIFICATION AND READING COMPREHENSION PERFORMANCE Source
df
SS
MS
F
ES
Experiment 1 (Character identification) Sex
1
1377.8
1377.8
92.18‡
0.194
Noise intensity (N)
2
2434.2
1217.1
81.83‡
0.342
Illumination intensity (I)
2
699.4
349.7
23.40‡
0.098
4
59.8
14.9
1.00
0.008
Error
170
2540.9
14.9
Total
179
7112.2
N*I
Experiment 2 (Reading comprehension) Sex
1
3.76
3.76
8.62
0.026
Noise intensity (N)
2
14.63
7.32
16.80‡
0.102
Illumination intensity (I)
2
44.43
22.22
51.01‡
0.311
4
5.93
1.48
3.41
0.041
170
74.04
0.44
Total 179 Note.—Effect size (ES) is partial η2. ‡p < .001.
142.80
N*I Error
Experiment 2 LONG-TERM VISUAL PERFORMANCE Participants, Apparatus, and Workplace Conditions One month after the completion of Experiment 1, the 20 students (10 women, 10 men; all had participated in Experiment 1) participated in Experiment 2. The experimental apparatus and workplace conditions were the same as those for Experiment 1. Task and Procedure The participants were instructed to perform a long-term reading task. Each individual experimental session consisted of the following sequence of events. Nine articles were used. Each article contained 23 screen-pages and each page was presented on the screen for 2 min. (46 min. for the entire article). The articles were assigned randomly for the nine treatments for each participant. The articles were in Chinese. These articles were selected from various e-books including romance, science fiction, fiction, and historical stories. The characters were displayed using the font “ET,” in 15 × 16-dot matrices. The height and width of the characters were approximately 5.3 mm × 5.6 mm, respectively. The characters for the text were presented in 18–20 lines, with 30 characters per line. The inter-character spacing was
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approximately 0.7 mm, and inter-line spacing was approximately 1.4 mm. The height and width of the area used for the text presentation were approximately 140 mm × 180 mm, respectively. The participants were required to read the article and then in 10 min. complete a 10-question comprehension test, in which each question included four options, with only one correct, at the end of the experimental session. The 10 questions were produced from the corresponding article. For each participant, two within-subjects factor treatments were administered randomly. Before the experiment, the treatment sequence for each participant was determined by drawing lots. Each treatment lasted for approximately 1 hr. for each participant. The experiment was scheduled according to the free time of the participants, so there were different sections (each section between 1–3 hr. long) for each participant. In order to maintain motivation, participants were paid NT$150 per hr., plus an extra NT$5 for each correct answer on the comprehension test. Dependent Measures and Data Analysis Long-term visual performance was defined as the number of correct answers for the reading comprehension test. Analysis of variance (ANOVA) was conducted using the Statistical Analysis System (SAS 9.0). Sex was also treated as a blocking effect in the analysis. RESULTS Reading comprehension for each level of independent variables are shown in Table 1. The results of the ANOVA for the reading comprehension for independent variables (Table 2) show that noise intensity (F2, 170 = 51.01, p < .0001) and illumination intensity (F2, 170 = 16.80, p < .0001) had a statistically significant effect on reading comprehension performance. The Duncan multiple paired comparisons (Table 1) showed that a noise level of 30 dBA (7.07) resulted in the best reading comprehension performance, followed by 60 dBA (6.48) and 90 dBA (5.85). In terms of illumination intensity, 500 lux (6.85) resulted in better reading comprehension performance than 200 lux (6.38) and 800 lux (6.17). The results of Experiment 2 also show that noise intensity and illumination intensity had a significant interaction effect (F4, 170 = 3.41, p = .01) on visual performance. Reading comprehension performance at illumination intensity 500 lux and noise intensity 30 dBA (7.1) was better than at illumination intensity 800 lux and noise intensity 90 dBA (5.65), as shown in Fig. 1. GENERAL DISCUSSION Noise Intensity The analysis of variance results show that noise intensity significantly affects visual performance. Duncan multiple paired-comparisons show
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450 Reading Comprehension Performance
C-C. LIN
7.0
6.5
6.0
5.5
0.0 30 dBA
60 dBA
90 dBA
Noise Intensity
FIG. 1. Interaction of noise intensity and illumination intensity for reading comprehension performance. Intensities 200 lux (dotted line), 500 lux (dashed line), 800 lux (circles, solid line).
that a noise level of 30 dBA resulted in the best visual performance, followed by 60 dBA and 90 dBA. High noise intensity resulted in lower visual performance. These results are similar to those obtained by Jahncke, et al. (2011) and Trimmel, et al. (2012) in that the noise is shown to impair task performance and lower noise intensity is better than high noise intensity. Four reasons are offered to explain these results. First, the participants might not have been able to hear the warning tone when the noise intensity was high, so they might have missed the warning tone, reducing preparation time to identify the stimuli and worsening the character identification performance. Second, high noise intensity might have impaired the participants’ attention (Jahncke, et al., 2011), which would reduce both character-identification and reading comprehension performance. Third, noise has been found to increase the mental workload imposed by a given task environment (Becker, Warm, Dember, & Hancock, 1995). Therefore, higher noise intensity might have resulted in higher mental workload and reduced visual performance. Fourth, noise might impair short-term (working) memory and thus reduced reading comprehension performance (Hockey & Hamilton, 1983). Illumination Intensity Analysis of variance results showed that illumination intensity significantly affected visual performance. Visual performance at normal illumination intensity was better than at other illumination intensities. These results are consistent with those obtained by Kubo, et al. (2000), Shieh and
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Lin (2000), and Chung and Lu (2003), in that illumination intensity was shown to significantly affect visual performance using a TFT-LCD. Normal (500 lux) illumination intensity results in the best visual performance. The screen luminance of a given TFT-LCD is affected by illumination intensity (Chung & Lu, 2003). High illumination intensity can cause screen images to fade (Ostberg, 1980), which may explain why an illumination intensity of 500 lux resulted in better visual performance than 800 lux (high illumination intensity). However, an illumination intensity of 200 lux (low illumination intensity) did not result in better visual performance than 500 lux (normal illumination intensity). There could be two reasons for this result. First, the effect of illumination intensity may be obscured if the illumination intensity is relatively low, because the luminance of the reflected illumination intensity and the decrease in the percentage of luminance contrast ratios are very slight. For example, the luminance reflected at 200 and 500 lux is less than 0.5 cd/m2. Therefore, the effect of the luminance of the reflected illumination intensity is obscured, although an illumination intensity of 500 lux may result in slightly greater direct reflected light than one of 200 lux (Isensee & Bennett, 1983). Second, an illumination intensity of 200 lux may cause more visual fatigue than one of 500 lux, which would decrease visual performance. Interaction of Noise Intensity and Illumination Intensity Figure 1 shows the interaction effect of noise intensity and illumination intensity on reading comprehension performance. At 30 dBA, the reading performances for different illumination intensities are very similar. When the noise intensity was increased to 60 dBA, the decrease in reading comprehension performance at 800 and 200 lux was greater than at 500 lux. Again, when the noise intensity increased to 90 dBA, the reading comprehension performance significantly decreased for all illumination intensities. These results are similar to those obtained by Bhattacharya, et al. (1989): the interaction of noise and illumination had a significant effect on visual performance. Two explanations are offered for these results. First, at low noise intensity, the participants can try to mitigate the effect of noise for different illumination intensities. Therefore, the effect of illumination intensity is not significant. Second, when the noise intensity exceeds the tolerance limit of the participants, the effect of illumination intensity becomes more obvious, so there is a decrease in the participants’ reading comprehension performance. Conclusion High noise intensity results in lower visual performance. The results of this study reveal that noise generally degrades visual performance.
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These findings are in general agreement with the previous empirical literature. Visual performance at normal illumination intensity is better than at other illumination intensities. Furthermore, when ambient noise intensity increases, reading comprehension performance significantly decreases at all illumination intensities. REFERENCES
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HELTON, W., MATTHEWS, G., & WARM, J. S. (2009) Stress state mediation between environmental variables and performance: the case of noise and vigilance. Acta Psycholgica, 130, 204-213. HOCKEY, G. R. J., & HAMILTON, P. (1983) The cognitive patterning of stress states. In G. R. J. Hockey (Ed.), Stress and human performance. Chichester, UK: Wiley. HORIKAWA, M. (2001) Effect of visual display terminal height on the trapezius muscle hardness: quantitative evaluation by a newly developed muscle hardness meter. Applied Ergonomics, 32, 473-478. ISENSEE, S. H., & BENNETT, C. A. (1983) The perception of flicker and glare on computer CRT. Display, 25(2), 177-184. JAHNCKE, H., HYGGE, S., HALIN, N., GREEN, A. M., & DIMBERG, K. (2011) Open-plan office noise: cognitive performance and restoration. Journal of Environmental Psychology, 31, 373-382. KUBO, M., UCHI, T., NARUTAKI, Y., SHINOMIYA, T., & ISHII, Y. (2000) Development of “Advanced TFT-LCD” with good legibility under any ambient light intensity. Journal of the Society for Information Display, 8, 299-304. LASZLO, H. E., MCROBIE, E. S., STANSFELD, S. A., & HANSELL, A. L. (2012) Annoyance and other reaction measures to changes in noise exposure—a review. Science of the Total Environment, 435-436, 551-562. LEE, D-S., KO, Y-H., SHEN, I-H., & CHAO, C-Y. (2011) Effect of light source, ambient illumination, character size and interline spacing on visual performance and visual fatigue with electronic paper displays. Displays, 32, 1-7. LIEBL, S., HALLER, J., JÖDICKE, B., BAUMGARTNER, H., SCHLITTMEIER, S., & HELLBRÜCK, J. (2012) Combined effects of acoustic and visual distraction on cognitive performance and well-being. Applied Ergonomics, 43, 424-434. LIN, C-C. (2005) Effects of screen luminance combination and text color on visual performance with TFT-LCD. International Journal of Industrial Ergonomics, 35, 229-235. LIN, C-C., & HUANG, K-C. (2006) Effects of ambient illumination and screen luminance combination on the character identification performance of desktop TFT-LCD monitors. International Journal of Industrial Ergonomics, 36, 211-218. LIN, C-J., FENG, W-Y., CHAO, C-J., & TSENG, F-Y. (2008) Effects of VDT workstation lighting conditions on operator visual workload. Industrial Health, 46, 105-111. LIN, P-H., LIN, Y-T., HWANG, S-L., JENG, S-C., & LIAO, C-C. (2008) Effects of anti-flare surface treatment, ambient illumination and bending curvature on legibility and visual fatigue of electronic papers. Displays, 29, 25-32. MELAMED, S., & BRUHIS, S. (1996) The effects of chronic industrial noise exposure on urinary cortisol, fatigue, and irritability. Journal of Occupational and Environmental Medicine, 38, 252-256. MONTGOMERY, D. C., & MONTGOMERY, D. C. (1997) Design and analysis of experiments. Vol. 7. New York: Wiley. MORRISON, W. E., HAAS, E. C., SHAFFNER, D. H., GARRETT, E. S., & FACKLER, J. C. (2003) Noise, stress, and annoyance in a pediatric intensive care unit. Critical Care Medicine, 31(1), 113-119. OSTBERG, O. (1980) Accommodation and visual fatigue in display work. In E. Grandjean and E. Vigliani (Eds.), Ergonomics aspects of visual display terminal. London, UK: Taylor and Francis.
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PARSONS, K. C. (2000) Environmental ergonomics: a review of principles, methods and models. Applied Ergonomics, 31, 581-594. PAWLAK, U. (1986) Ergonomic aspects of image polarity. Behaviour and Information Technology, 5, 335-348. RYHERD, E. E., WAYE, K. P., & LJUNGKVIST, L. (2008) Characterizing noise and perceived work environment in a neurological intensive care unit. Journal of the Acoustical Society of America, 123(2), 747-756. SHIEH, K-K., & LIN, C-C. (2000) Effects of screen type, ambient illumination, and color combination on VDT visual performance and subjective preference. International Journal of Industrial Ergonomics, 26, 527-536. TADAHIKO, F. (1992) Visual capability to receive character information, Part І: How many characters can we recognize at a glance? Ergonomics, 35(5/6), 617-627. TAKAHASHI, K., SASAKI, H., SAITO, T., HOSOKAWA, T., KURASAKI, M., & SAITO, K. (2001) Combined effects of working environmental conditions in VDT work. Ergonomics, 44(5), 562-570. TRIMMEL, M., ATZLSDORFER, J., TUPY, N., & TRIMMEL, K. (2012) Effects of low intensity noise from aircraft or from neighbourhood on cognitive learning and electrophysiological stress responses. International Journal of Hygiene and Environmental Health, 215, 547-554. TSENG, F-Y., CHAO, C-J., FENG, W-Y., & HWANG, S-L. (2010) Assessment of human color discrimination based on illuminant color, ambient illumination and screen background color for visual display terminal workers. Industrial Health, 48, 438-446. TURVILLE, K. L., PSIHOGIOS, J. P., UIMER, T. R., & MIRKA, G. A. (1998) The effects of video display terminal height on the operator: a comparison of the 15° and 40° recommendations. Applied Ergonomics, 29, 239-246. WICKENS, C. D. (1992) Engineering psychology and human performance. (2nd ed.) New York: Harper Collins. YOSHIDA, Y., & YAMAMOTO, Y. (2002) Color management of liquid crystal display placed under light environment. Electronics and Communications in Japan, 86(7), J85-A (D. J. T. G. Ronbunshi, Transl.), 793-805. Accepted July 21, 2014.
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EFFECT OF NOISE INTENSITY AND ILLUMINATION INTENSITY ON VISUAL PERFORMANCE1, 2 CHIN-CHIUAN LIN Department of Business Administration, Kun-Shan University Summary.—The results of Experiment 1 indicated that noise and illumination intensity have a significant effect on character identification performance, which was better at 30 dBA than at 60 and 90 dBA, and better at 500 and 800 lux than at 200 lux. However, the interaction of noise and illumination intensity did not significantly affect visual performance. The results of Experiment 2 indicated that noise and illumination intensity also had a significant effect on reading comprehension performance, which was better at 30 dBA than at 60 and 90 dBA, and better at 500 lux than at 200 and 800 lux. Furthermore, reading comprehension performance was better at 500 lux lighting and 30 dBA noise than with 800 lux and 90 dBA. High noise intensity impaired visual performance, and visual performance at normal illumination intensity was better than at other illumination intensities. The interaction of noise and illumination had a significant effect on reading comprehension. These results indicate that noise intensity lower than 30 dBA and illumination intensity approximately 500 lux might be the optimal conditions for visual work.
Over the past decade, thin film transistor liquid-crystal displays (TFTLCDs) have become popular for information displays because of the rapid decrease in their price, their lower power consumption, their improved optical characteristics, and the increased size of displays. Generally, work in an office environment is affected by noise intensity and illumination intensity. Noise may negatively affect workers using TFT-LCDs; many studies have shown that noise can affect task performance by adding to the overall mental workload (Parsons, 2000). Studies have indicated that noise has a significant effect on the human reactions, such as sleep disturbance, activity disturbance, and uses of the environment (Laszlo, McRobie, Stansfeld, & Hansell, 2012). Effect of Noise on Work Hockey and Hamilton (1983) summarized the effect of noise on different components of performance, and found that the accuracy of responses was degraded. Furthermore, noise was also found to have a detrimental effect on short-term (working) memory function. Hancock, Conway, Address correspondence to Chin-Chiuan Lin, Department of Business Administration, KunShan University, 195, Kun-Da Road, Yung-Kang District, Tainan City, 710, Taiwan (R.O.C.) or e-mail ([email protected]). 2 This study was supported by a research grant from the National Science Council of the Republic of China, Grant No. NSC 101-2221-E-168-036. 1
DOI 10.2466/26.24.PMS.119c20z1
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ISSN 0031-5125
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Szalma, Ross, and Saxton (2005) indicated that noise had a moderate but deleterious effect on performance, though it had differential effects on various tasks. Jahncke, Hygge, Halin, Green, and Dimberg (2011) studied the cognitive and emotional effect of noisy conditions during work, using a simulated open-plan office. The results showed that the participants remembered fewer words and rated themselves less motivated to work with a high noise level than with a low level. Trimmel, Atzlsdorfer, Tupy, and Trimmel (2012) studied the effect of low intensity noise on cognitive learning and electrophysiological stress responses. Their data demonstrated a significant effect on cognition and physiology of low intensity background noise. The results showed an impairment of reproduction (cognition) and increased heart rate in noisy conditions. Clark, Head, and Stansfeld (2013) showed that exposure to noise can impair reading comprehension and increase noise annoyance, but does not affect the psychological health of children. Noise can affect not only psychological responses, but also affect physiological responses of humans. Morrison, Haas, Shaffner, Garrett, and Fackler (2003) studied the correlation between noise and stress in nurses and concluded that noise is a potentially significant contributor to elevated heart rate and tachycardia. Ryherd, Waye, and Ljungkvist (2008) used a questionnaire to examine nurses' perceptions of noisy environments. They found that 66% of nurses experienced irritation and fatigue, 43% had concentration problems, and 40% experienced tension headaches. Chang, Liu, Hsieh, Bao, and Lai (2012) found that over 24 hours, a 1-dBA increment in environmental noise was statistically associated with a sustained 1.25% mL/mmHg increase in arterial compliance, but a 2.12 kdyne·sec/cm3 decrease in arterial resistance. They concluded that environmental noise exposure can have both transient and sustained effects on adults' vascular condition. Chang, Hwang, Liu, Chen, and Wang (2013) indicated that prolonged exposure to noise levels above 85 dBA may increase males' systolic and diastolic blood pressure. Hancock, et al. (2005) indicated that noise degraded performance in two taxons, information processing and problem solving, both related to cognition. They also indicated that a vast amount of effort has been expended on the investigation of the effects of noise on non-auditory aspects of performance; information processing, attention, and memory typically can be affected to some extent. Conversely, Helton, Matthews, and Warm (2009) showed that vigilance in noisy conditions was significantly better than in quiet conditions. They also found that the presence of noise increased self-reported task engagement and improved performance. Furthermore, in his review, Parsons (2000) concluded that the effect of noise on non-auditory task performance was inconclusive, as different studies
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have indicated that noise reduces task performance, has no effect on task performance, or increases task performance. However, these inconsistent results for the effect of noise on workers' performance might be due to the different type of tasks (vigilance or cognitive) evaluated. Despite the inconsistent results for the effect of noise on performance, no study has found any quantifiable relationship between noise intensity and work performance (Hancock, et al., 2005). Higher noise intensity might reduce cognitive resources, thereby reducing visual performance. The effect of noise intensity on visual performance remains a topic of study. Effect of Illumination on Work Visual performance has many aspects (e.g., cognition, identification, and detection) and is used in varied tasks such as reading, proofreading, vigilance, and searching. Regardless of the indicators, according to the model of human information processing (Wickens, 1992), visual tasks can be divided into two categories: short-term (perception tasks which need response within a short time) and long-term (decision and selection tasks with relatively long processing times). Nearly all jobs require effectiveness and efficiency at both these types of tasks. Illumination intensity significantly affects human responses such as visual performance (Lin, 2005; Lee, Ko, Shen, & Chao, 2012), color discrimination (Yoshida & Yamamoto, 2002; Tseng, Chao, Feng, & Hwang, 2010), and visual workload (Lin, Feng, Chao, & Tseng, 2008). When a TFTLCD display is placed in a lighted environment, surface reflections are produced. Although the surface of a TFT-LCD screen has an antiglare polarizer (Choi & Miyasaka, 1993), it still reflects the lighting (Kubo, Uchi, Narutaki, Shinomiya, & Ishii, 2000) in proportion to the intensity of the lighting. Therefore, illumination intensity is an important consideration in workplace design.3 The illumination conditions for information-gathering work can also vary greatly. Hypothetically, either too low or too high illumination intensity might reduce visual performance. In short, there might be an optimal illumination intensity for visual work. Therefore, there is a need to further examine the effect of illumination intensity on visual performance. Pawlak (1986) proposed that the reflective property of a screen could be expressed as a reflection factor (q). The reflected illumination (RI) is equal to the reflection factor (q) multiplied by the illumination intensity (II). The luminance of reflected light (RL) is equal to the reflected illumination (RI) divided by π (RL = RI/π) (Eperjesi, Fowler, & Kempster, 1995). Therefore, the luminance of reflected light (RL) is equal to the reflection factor, which is obtained through multiplying the illumination by the reflected illumination intensity (RL = q*II/π). When the level of illumination intensity increases, the reflected light increases proportionally (Chung & Lu, 2003). 3
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Noise/Illumination Interaction Bhattacharya, Tripathi, and Kashyap (1989) found that the interaction of noise and reflected illumination has a significant effect on accuracy scores for a letter-cancellation test and a hand-precision test and on efficiency scores for the latter test. Takahashi, Sasaki, Saito, Hosokawa, Kurasaki, and Saito (2001) reported that a high luminance with noise had the most effect on subjective fatigue and mental activities in visual work. Liebl, Haller, Jödicke, Baumgartner, Schlittmeier, and Hellbrück (2012) found that even a low level of intelligible background speech significantly impaired short-term memory, reasoning ability, and well-being. In addition, the interaction of background speech and lighting conditions was found to affect perceived performance. However, no effect on cognitive performance was found. Because studies on the effect of the interaction between noise intensity and illumination intensity on visual work have not quantified the effect of the interaction between noise intensity and illumination intensity on visual performance, more work is needed. Research goal. To specify the effects of noise intensity and illumination intensity on visual performance (character identification and reading comprehension). Hypothesis 1. Higher noise intensity and either too low or too high illumination intensity will reduce visual performance. Experiment 1 SHORT-TERM VISUAL PERFORMANCE METHOD Participants Twenty (10 women, 10 men) students from Kun-Shan University were enrolled as participants (age range = 19–23 yr.). All had at least 0.8 corrected visual acuity or better and normal vision. Procedure This study evaluated two independent variables: noise intensity and illumination intensity. The experiment used three levels of noise intensity: 30 dBA (below the level used by Jahncke, et al., 2011), 60 dBA, and 90 dBA (the permissible exposure level at the workplace in Taiwan; Melamed & Bruhis, 1996). The noise used here was a recording from a bolt factory in Tainan, which was repeatedly (1 hr. cycles) broadcast by loudspeakers (1000 W), with the speakers located about 1.5 m behind the participants. Three levels of illumination intensity were used: 200 lux (low-level office illumination; Helander & Rupp, 1984), 500 lux (normal office of illumination; ANSI/IES, 1983), and 800 lux (high-level office illumination; Lin &
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Huang, 2006). Gray was used for the text and the background color, in order to prevent chromatic aberration (Charman, 1991). The luminance of the background was 40 cd/m2 and the luminance of the text was 5 cd/m2. Therefore, the luminance contrast was 8:1 (Lin, 2005) and the polarity was positive (Buchner & Baumgartner, 2007). All of the participants completed nine within-subjects experimental treatment combinations (3 noise intensity × 3 illumination intensity) for Experiment 1. Apparatus and Workplace Conditions A 17 in., CMV 745A TFT-LCD with a 433 mm diagonal screen provided an active viewing area of 338 mm horizontally and 272 mm vertically. The pixel resolution was 1024 horizontally and 768 vertically, and the center-to-center pixel spacing was approximately 0.35 mm. The screen images were refreshed at a rate of 72 Hz. The maximal luminance contrast ratio value and the maximal luminance of the TFT-LCD were approximately 150 and 210 cd/m2, respectively. The screen surface was coated with a SiO2 polarizer to reduce glare and reflection. The fluorescent lamps used were 40W FL40D/38, purchased from Taiwan Light Co. (Taiwan). The CIE (International Commission on Illumination; Commission internationale de l′éclairage) values for the TFT-LCD screen were measured using a Laiko Color Analyzer, DT-100 purchased from Laiko Co., Ltd. (Taiwan). The illumination intensity was measured using a TES-1330 digital lux meter purchased from TES Electronic Co. (Taiwan), and the noise intensity was measured using a NM102 noise meter purchased from NoiseMeters Co., Ltd. (UK). A Topcon SS-3 Screenscope was used to test the visual acuity of the participants. The experiment was held in a soundproof and light-obstructed room. The TFT-LCD was positioned on a table 70 cm in height (Horikawa, 2001). The inclination angle of the TFT-LCD screen was 105º (Turville, Psihogios, Uimer, & Mirka, 1998; Burgess-Limerick, Mon-Williams, & Coppard, 2000) with respect to the vertical axis. A headrest restrained each participant's head at 25 cm above the table and ensured a constant viewing distance of 55 cm during the experiment. There was no glare on the TFT-LCD screen. Task and Procedure The participants completed a character identification task. At the beginning of each trial a warning tone was sounded, to instruct the subject to visually fixate on an “X” at the center of the screen. A few seconds later (a uniform distribution from 2 to 6 sec.), a stimulus that was composed of four (Tadahiko, 1992) English capital letters was presented in the center area of the screen for 200 msec. (approximately one eye-fixation duration). The participants were required to write as many letters as they could
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identify, in their corresponding position, within 10 sec. The four English capital letters were presented at the corners of an area at the center of the screen that measured approximately 20 mm × 25 mm. The height of the 12-point letters was approximately 4.2 mm. The subtended visual angle of the letters was approximately 25 minutes of arc. One capital English letter (A-Z) occupied each position. Each letter was presented once in each position. There were 26 trials for each treatment. For the first trial, the computer randomly selected one English capital letter from the letter set for each of the positions: 1, 2, 3, and 4, respectively. These letters were then deleted from the letter set for that position and recorded in a file. In the second trial, the computer again randomly selected one letter from the remaining letter sets for positions 1, 2, 3, and 4. The procedure was repeated iteratively, until the letter sets were empty. In order to familiarize the participants with the character identification task, they were required to perform five training trials for each treatment. Each treatment required approximately 4 min. There was a 1 to 2 min. break between treatments, to avoid successive contrast effects and visual fatigue. The overall experiment lasted for approximately 1 hr. for each participant, including regular breaks to reduce fatigue. For each participant, the two within-subjects factor treatments were administered randomly. Before the experiment, the treatment sequence for each participant was determined by drawing lots. In order to maintain motivation, the participant were paid NT$150 per hour, plus an extra NT$150 if their overall average percentage of correctly identified letters exceeded 60%. Performance Measures and Data Analysis The percentage of correctly identified letters was used as the measure of short-term visual performance. The correctness was determined by the recalled position sequence. For example, if the presented position sequence for a particular trial was “BMSQ,” then the response “MSQB” was deemed to be incorrect. Previous studies have found that a range of illumination intensity between 50 and 3,000 lux does not affect a change in either physiological measures or in self-reported fatigue (Buchner & Baumgartner, 2007; Lin, Lin, Hwang, Jeng, & Liao, 2008). Therefore, this study did not measure the physiological responses or self-reported fatigue. Analysis of variance (ANOVA) was conducted, using the Statistical Analysis System (SAS 9.0). Sex was treated as a blocking effect (Montgomery & Montgomery, 1997) in the analysis. RESULTS The character identification performance values for each level of independent variables are shown in Table 1. The interaction effect of noise in-
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VISUAL PERFORMANCE TABLE 1 CHARACTER IDENTIFICATION AND READING COMPREHENSION PERFORMANCE FOR EACH NOISE AND ILLUMINATION INTENSITY AND DUNCAN GROUPING Independent Variable
n
Mean
SD
Duncan Grouping
Experiment 1 (Character identification) Sex Female
90
73.87
6.50
A
Male
90
68.33
4.71
B
30 dBA
60
75.78
5.70
A
60 dBA
60
70.72
5.20
B
90 dBA
60
66.80
4.44
C
500 lux
60
73.00
6.02
A
800 lux
60
71.92
5.74
A
200 lux
60
68.38
6.28
B
Noise intensity
Illumination intensity
Experiment 2 (Reading comprehension) Sex Female
90
6.61
0.83
A
Male
90
6.32
0.93
B
30 dBA
60
7.07
0.71
A
60 dBA
60
6.48
0.75
B
90 dBA
60
5.85
0.78
C
500 lux
60
6.85
0.76
A
200 lux
60
6.38
0.88
B
800 lux
60
6.17
0.91
B
Noise intensity
Illumination intensity
tensity and illumination intensity on character identification performance was not statistically significant (Table 2). The results in Table 2 also indicate that the main effects for noise intensity (F2, 170 = 81.43, p < .0001) and illumination intensity (F2, 170 = 23.40, p < .0001) were statistically significant. The Duncan multiple paired-comparisons (Table 1) show that a noise level of 30 dBA (75.78%) resulted in the best character identification performance, followed by 60 dBA (70.72%) and 90 dBA (66.80%). In terms of illumination intensity, 500 lux (73.0%) and 800 lux (71.92%) resulted in better character identification performance than 200 lux (68.38%). Overall, 30 dBA and 500 lux resulted in the best character identification performance (78.15%) and 90 dBA and 200 lux resulted in the worst performance (63.95%).
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C-C. LIN TABLE 2 ANOVA FOR CHARACTER IDENTIFICATION AND READING COMPREHENSION PERFORMANCE Source
df
SS
MS
F
ES
Experiment 1 (Character identification) Sex
1
1377.8
1377.8
92.18‡
0.194
Noise intensity (N)
2
2434.2
1217.1
81.83‡
0.342
Illumination intensity (I)
2
699.4
349.7
23.40‡
0.098
4
59.8
14.9
1.00
0.008
Error
170
2540.9
14.9
Total
179
7112.2
N*I
Experiment 2 (Reading comprehension) Sex
1
3.76
3.76
8.62
0.026
Noise intensity (N)
2
14.63
7.32
16.80‡
0.102
Illumination intensity (I)
2
44.43
22.22
51.01‡
0.311
4
5.93
1.48
3.41
0.041
170
74.04
0.44
Total 179 Note.—Effect size (ES) is partial η2. ‡p < .001.
142.80
N*I Error
Experiment 2 LONG-TERM VISUAL PERFORMANCE Participants, Apparatus, and Workplace Conditions One month after the completion of Experiment 1, the 20 students (10 women, 10 men; all had participated in Experiment 1) participated in Experiment 2. The experimental apparatus and workplace conditions were the same as those for Experiment 1. Task and Procedure The participants were instructed to perform a long-term reading task. Each individual experimental session consisted of the following sequence of events. Nine articles were used. Each article contained 23 screen-pages and each page was presented on the screen for 2 min. (46 min. for the entire article). The articles were assigned randomly for the nine treatments for each participant. The articles were in Chinese. These articles were selected from various e-books including romance, science fiction, fiction, and historical stories. The characters were displayed using the font “ET,” in 15 × 16-dot matrices. The height and width of the characters were approximately 5.3 mm × 5.6 mm, respectively. The characters for the text were presented in 18–20 lines, with 30 characters per line. The inter-character spacing was
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approximately 0.7 mm, and inter-line spacing was approximately 1.4 mm. The height and width of the area used for the text presentation were approximately 140 mm × 180 mm, respectively. The participants were required to read the article and then in 10 min. complete a 10-question comprehension test, in which each question included four options, with only one correct, at the end of the experimental session. The 10 questions were produced from the corresponding article. For each participant, two within-subjects factor treatments were administered randomly. Before the experiment, the treatment sequence for each participant was determined by drawing lots. Each treatment lasted for approximately 1 hr. for each participant. The experiment was scheduled according to the free time of the participants, so there were different sections (each section between 1–3 hr. long) for each participant. In order to maintain motivation, participants were paid NT$150 per hr., plus an extra NT$5 for each correct answer on the comprehension test. Dependent Measures and Data Analysis Long-term visual performance was defined as the number of correct answers for the reading comprehension test. Analysis of variance (ANOVA) was conducted using the Statistical Analysis System (SAS 9.0). Sex was also treated as a blocking effect in the analysis. RESULTS Reading comprehension for each level of independent variables are shown in Table 1. The results of the ANOVA for the reading comprehension for independent variables (Table 2) show that noise intensity (F2, 170 = 51.01, p < .0001) and illumination intensity (F2, 170 = 16.80, p < .0001) had a statistically significant effect on reading comprehension performance. The Duncan multiple paired comparisons (Table 1) showed that a noise level of 30 dBA (7.07) resulted in the best reading comprehension performance, followed by 60 dBA (6.48) and 90 dBA (5.85). In terms of illumination intensity, 500 lux (6.85) resulted in better reading comprehension performance than 200 lux (6.38) and 800 lux (6.17). The results of Experiment 2 also show that noise intensity and illumination intensity had a significant interaction effect (F4, 170 = 3.41, p = .01) on visual performance. Reading comprehension performance at illumination intensity 500 lux and noise intensity 30 dBA (7.1) was better than at illumination intensity 800 lux and noise intensity 90 dBA (5.65), as shown in Fig. 1. GENERAL DISCUSSION Noise Intensity The analysis of variance results show that noise intensity significantly affects visual performance. Duncan multiple paired-comparisons show
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450 Reading Comprehension Performance
C-C. LIN
7.0
6.5
6.0
5.5
0.0 30 dBA
60 dBA
90 dBA
Noise Intensity
FIG. 1. Interaction of noise intensity and illumination intensity for reading comprehension performance. Intensities 200 lux (dotted line), 500 lux (dashed line), 800 lux (circles, solid line).
that a noise level of 30 dBA resulted in the best visual performance, followed by 60 dBA and 90 dBA. High noise intensity resulted in lower visual performance. These results are similar to those obtained by Jahncke, et al. (2011) and Trimmel, et al. (2012) in that the noise is shown to impair task performance and lower noise intensity is better than high noise intensity. Four reasons are offered to explain these results. First, the participants might not have been able to hear the warning tone when the noise intensity was high, so they might have missed the warning tone, reducing preparation time to identify the stimuli and worsening the character identification performance. Second, high noise intensity might have impaired the participants’ attention (Jahncke, et al., 2011), which would reduce both character-identification and reading comprehension performance. Third, noise has been found to increase the mental workload imposed by a given task environment (Becker, Warm, Dember, & Hancock, 1995). Therefore, higher noise intensity might have resulted in higher mental workload and reduced visual performance. Fourth, noise might impair short-term (working) memory and thus reduced reading comprehension performance (Hockey & Hamilton, 1983). Illumination Intensity Analysis of variance results showed that illumination intensity significantly affected visual performance. Visual performance at normal illumination intensity was better than at other illumination intensities. These results are consistent with those obtained by Kubo, et al. (2000), Shieh and
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Lin (2000), and Chung and Lu (2003), in that illumination intensity was shown to significantly affect visual performance using a TFT-LCD. Normal (500 lux) illumination intensity results in the best visual performance. The screen luminance of a given TFT-LCD is affected by illumination intensity (Chung & Lu, 2003). High illumination intensity can cause screen images to fade (Ostberg, 1980), which may explain why an illumination intensity of 500 lux resulted in better visual performance than 800 lux (high illumination intensity). However, an illumination intensity of 200 lux (low illumination intensity) did not result in better visual performance than 500 lux (normal illumination intensity). There could be two reasons for this result. First, the effect of illumination intensity may be obscured if the illumination intensity is relatively low, because the luminance of the reflected illumination intensity and the decrease in the percentage of luminance contrast ratios are very slight. For example, the luminance reflected at 200 and 500 lux is less than 0.5 cd/m2. Therefore, the effect of the luminance of the reflected illumination intensity is obscured, although an illumination intensity of 500 lux may result in slightly greater direct reflected light than one of 200 lux (Isensee & Bennett, 1983). Second, an illumination intensity of 200 lux may cause more visual fatigue than one of 500 lux, which would decrease visual performance. Interaction of Noise Intensity and Illumination Intensity Figure 1 shows the interaction effect of noise intensity and illumination intensity on reading comprehension performance. At 30 dBA, the reading performances for different illumination intensities are very similar. When the noise intensity was increased to 60 dBA, the decrease in reading comprehension performance at 800 and 200 lux was greater than at 500 lux. Again, when the noise intensity increased to 90 dBA, the reading comprehension performance significantly decreased for all illumination intensities. These results are similar to those obtained by Bhattacharya, et al. (1989): the interaction of noise and illumination had a significant effect on visual performance. Two explanations are offered for these results. First, at low noise intensity, the participants can try to mitigate the effect of noise for different illumination intensities. Therefore, the effect of illumination intensity is not significant. Second, when the noise intensity exceeds the tolerance limit of the participants, the effect of illumination intensity becomes more obvious, so there is a decrease in the participants’ reading comprehension performance. Conclusion High noise intensity results in lower visual performance. The results of this study reveal that noise generally degrades visual performance.
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