|Year : 2018 | Volume
| Issue : 2 | Page : 98-104
Effect of below-damage-risk criteria environmental noise on auditory perception and working memory
Sandeep Maruthy, G Nike Gnanateja, Preethi C Chengappa, Sam A Publius, Varsha M Athreya
Department of Audiology, All India Institute of Speech and Hearing, Mysore, Karnataka, India
|Date of Web Publication||4-Sep-2018|
Mr. G Nike Gnanateja
Department of Audiology, All India Institute of Speech and Hearing, Mysore - 570 006, Karnataka
Source of Support: None, Conflict of Interest: None
Background: The current research finding is the first in reporting impaired auditory and cognitive abilities consequent to chronic exposure to below-damage-risk criteria (DRC) environmental noise in humans. Objective: The objective of this study is to assess the chronic effects of occupational noise below DRC on auditory and cognitive abilities. Methods: A static-group comparison design was used, with three groups with varying levels of noise exposure below DRC. Shopkeepers working in busy areas exposed daily to environmental noise below DRC and shopkeepers working in quiet residential areas and college students in quiet environments. Speech perception in noise, acceptable noise levels, and concurrent vowel identification were used to assess auditory abilities, while Operation SPAN and Backward Digit Span were used to assess cognitive abilities. The data were analyzed using multivariate analysis of variance, Pearson's product-moment correlation, discriminant function analysis, and mediation analysis. Results: The results showed significantly poor auditory stream segregation abilities and working memory abilities in the shopkeepers exposed to environmental noise below DRC when compared to the other two groups with very low levels of occupational noise. The findings of the study are discussed in light of the deleterious effect of the supposedly safe (below DRC) levels of environmental noise on auditory and cognitive abilities. Conclusions: The novel results of affected auditory and cognitive abilities resulting from below-DRC occupational noise exposure as observed in the current study will have a great impact on the applicability to the general populace and also open up new avenues of research in ecological acoustics.
Keywords: Below damage-risk criteria, occupational noise, stream segregation, working memory
|How to cite this article:|
Maruthy S, Gnanateja G N, Chengappa PC, Publius SA, Athreya VM. Effect of below-damage-risk criteria environmental noise on auditory perception and working memory. Indian J Otol 2018;24:98-104
|How to cite this URL:|
Maruthy S, Gnanateja G N, Chengappa PC, Publius SA, Athreya VM. Effect of below-damage-risk criteria environmental noise on auditory perception and working memory. Indian J Otol [serial online] 2018 [cited 2019 Feb 23];24:98-104. Available from: http://www.indianjotol.org/text.asp?2018/24/2/98/240571
| Introduction|| |
Exposure to intense levels of noise has been well documented to cause damage to the peripheral auditory system. This is often attributed to the damage to the hair cells and spiral ganglia. Such effects of noise on hearing abilities have led to the framing of several criteria for noise regulation to prevent the noise effects in humans. Most of them are generally applicable to occupational noise such as industrial noise, aircraft noise, and firecrackers. The noise levels in the environment are steadily increasing due to increasing traffic, listening to music, increasing population, urbanization, etc. These noises may not exceed the damage-risk criteria (DRC) prescribed by several committees. However, the duration of such environmental noise has increased due to the changes listed above. Such chronic exposure to supposedly safe (below DRC) levels of noise has to be assessed for their effects on auditory health.
The criteria for safe levels of noise exposure have majorly focused on permanent threshold shift in hearing thresholds resulting due to damage of the outer and inner hair cells, and auditory nerve. Changes in hearing sensitivity are major concerns for hearing health. However, the changes in higher-order processing which could happen due to noise exposure without causing a permanent hearing threshold shift should not be ignored. Several studies have shown spared hearing sensitivity but altered higher-order auditory and cognitive processing following noise exposure.
Kujawa and Liberman  subjected mice to octave band of noise 8–16 kHz at 100 dB SPL for 2 h (below DRC). Auditory brain stem responses and otoacoustic emissions were recorded 24 h postexposure and observed 40 dB elevation at high frequencies in auditory brain stem response and smaller elevation of thresholds in distortion product otoacoustic emissions. The thresholds came back to normal levels after 2 weeks of noise exposure. Although thresholds came back to normal, suprathreshold responses showed decrements which suggested loss of neurons at some of the cochlear regions as there was a dramatic degeneration of presynaptic and postsynaptic elements in the inner hair cell region.
Noreña et al. subjected juvenile cats to passive exposure of tone pips of 32 different frequencies (5–20 kHz), presented in random order at an average rate of 96 Hz at 80 dB SPL for 5 months. Postexposure, it was observed that cortical representation of the exposure frequencies was poorer, and there was secondary cortical re-organization. Similar effects were found in adult cats by Pienkowski, Munguia, and Eggermont (2011) for noise levels of 68 dB SPL. In their study, the cats were exposed to band-limited ensembles for about 6 weeks, 12 h per day. The effects in both the studies were evident in the absence of peripheral hearing loss.
In humans, Kumar et al. recorded temporal processing skills and speech perception in noise (SPIN) in normal hearing train drivers, exposed to occupational engine noise. Their results showed that speech recognition scores in the presence of noise were significantly poorer compared to control individuals, and it had an association with their poorer temporal processing skills. Similarly, Ganesan and Kumar  showed that individuals using personal music systems had poorer SPIN compared to those not using them on a daily basis. These studies along with those in animals show that higher-order auditory processing is affected in the long term even if there is no significant change in hearing thresholds. Although both these studies by Kumar et al. indicate that SPIN becomes poorer in individuals exposed to noise, the reports are of those exposed to noise levels above the DRC, and they do not inform about stream segregation skills of those exposed to noise levels below DRC. Auditory stream segregation is an important mechanism, which aids in tagging onto the desired auditory information stream while ignoring/separating the undesired background acoustic information such as traffic noise and chatter.
Verbal communication in noisy environments is a frequently encountered situation and yet is a challenging task. The challenge is because the competing noise often has overlapping acoustic properties with the target signal and requires the auditory system to segregate the target signal from noise. This is achieved by taking advantage of predictable and repeating nature of the speech signal (the pitch of the voice) amid the random, fluctuating background of many voices.,
SPIN has been known to be regulated by both afferent and efferent auditory system.,,,, Considering that these reports evidence training-related neural plasticity,, it is logical to assume that individuals functioning in noisy environments on a daily basis (such as shopkeepers in a busy market) shall possess better SPIN compared to those in a quiet environment, providing that the noise levels are not hazardous. An alternate line of thought could be that the exposure to noise might lead to reduction in auditory and cognitive performance as stated by the maximal adaptability theory.
It can be seen that the effects of below-DRC noise exposure can manifest in the auditory system despite the peripheral hearing sensitivity being intact. Such alterations in the higher auditory system in spite of normal hearing sensitivity have been demonstrated in children exposed to aircraft noise (below-DRC levels).,
Several studies have shown the importance of working memory in auditory perception, more so in speech in noise perception., Working memory refers to the component of memory responsible for temporary storage and manipulation of information necessary for perception, planning, reasoning and action, and learning., There are studies on below-DRC environmental noise exposure that have looked at auditory and cognitive performance of children exposed to aircraft noise and road traffic noise.,, They found impaired reading abilities and long-and short-term memory in the children exposed to aircraft noise. These findings, however, cannot be generalized to adults, as in these studies, the impact of noise was assessed in the developing auditory system in which the noise exposure may have a more pervasive effect on the entire nervous system. In adults, however, with developed auditory neural pathways, the effect may not be the same. In addition, the previous studies have assessed the effect of environmental noise exposure for a span of <2 years in children, while to the best of our knowledge, there is no literature on the effects of chronic noise exposure in adults. It is of interest to know if the long-term exposure to environmental noise below the prescribed DRC in any way affects the auditory and cognitive performance in adults.
Recently, Zhou and Merzenich  exposed adult rats to structured noise at a sound pressure level of 65 dB. This noise level was chosen as it is markedly below the broadly accepted safety level standard. Results showed that there were substantial negative consequences for the auditory system documented at the cortical level, attributable to environmental exposure to structured noises delivered under conditions that do not directly impact hearing sensitivity. These effects were present even with 10-h daily exposure. Considering these effects, humans chronically exposed to environmental noise for long hours on a daily basis may also develop auditory processing deficits.
Shopkeepers working in busy markets are continuously exposed to environmental noise and as a routine will have to perceive speech in the presence of noise. On a regular day, they function approximately for 10 h in such environment for several years. Going by the plastic nature of the auditory neural pathway, the physiological systems regulating SPIN should get fine-tuned over the years and should become more facilitative for SPIN. However, this assumed direction of relationship not necessarily has to be correct, considering that they are exposed to noise (not desirable sounds). The facilitative neuroplastic changes reported secondary to music exposure, and novel contrast learning may not be applicable in case of environmental noise exposure below DRC. Several years of exposure to this noise may not show a typical facilitative behavior but may show a negative influence. Therefore, to scientifically investigate the influence of below-DRC environmental noise exposure on auditory perceptual abilities, namely, SPIN and stream segregation skills, and working memory abilities, the present study was taken up.
Objective of the study
The objective of this study was to compare SPIN and stream segregation abilities of individuals chronically exposed to a noisy work environment below the DRC with those in relatively quiet work environment.
The present study used a static-group comparison research design and tested the null hypothesis that there is no significant difference between individuals with and without environmental noise exposure in their SPIN, stream segregation skills, and working memory abilities. All the procedures and methods conformed to the “Ethical Guidelines for Bio-Behavioral Research Involving Human Subjects” of the All India Institute of Speech and Hearing and also to the declaration of Helsinki.
Seventy-five normal hearing adults in the age range of 17–53 years participated in the study. All the participants had hearing thresholds below 15 dBHL. They were divided into three groups based on their work environment and work profile. Group 1 (n = 30) consisted of shopkeepers in a busy market who were working in noisy environment on a daily basis. This group was operationally called noise-exposed shopkeepers (NES) group. The noise levels (Leq) on an average during the peak hours was 76.5 dBA which was well below the DRC. Group 2 (n = 15) consisted of shopkeepers working in quiet residential areas, operationally termed shopkeepers in quiet (SQ) group. The noise levels (Leq) in the quiet residential areas did not exceed 67 dBA. Group 3 (n = 30) consisted of graduate students (Grad group). Participants in SQ and Grad groups did not function in noisy environments on a daily basis. These two groups served as control groups. While SQ group had the same work profile as NES group, Grad group differed both in terms of work profile and environment. It was ascertained from an interview that none of the selected participants reported difficulty in understanding speech in daily listening conditions and that they did not have any history of neurologic or otologic problems. An informed consent was taken before their participation in the study.
The primary purpose of the present study was to compare auditory and working memory abilities in the three target groups. For this purpose, we used auditory stream segregation tasks and noise tolerance measures which are apt to assess auditory abilities of ecological relevance. Thus, each participant was individually tested for their speech-to-noise ratio for 50% correct perception (SNR-50), acceptable noise level (ANL), concurrent vowel identification (CCV), Operation SPAN (OSPAN) and Backward Digit Span in a quiet room. SNR-50 and CCV were the indices for auditory stream segregation while OSPAN and Backward Digit Span were the indices of working memory.
Procedure for determining SNR-50
List 1 of QuickSIN-K test  comprising 7 sentences was used for this test. The first sentence had an SNR of 8 dB, and it was reduced in 3 dB steps for each of the following sentences. The participants were instructed to ignore the speech babble and repeat the main sentence verbatim. Each sentence had 5 keywords, and a score of 1 was given for each correct key word repeated. Finally, the score for all 7 sentences was calculated and subtracted from a value of 9.5 (highest SNR + half of step size) to get the SNR-50 value (in dB).
Procedure of Acceptable Noise Levels
The procedure was as recommended by Nabelek et al. A Kannada story was used as the speech stimulus where the participant had to indicate the most comfortable level (MCL). Then, white noise was presented to the same ear, starting at a level 15 dB below MCL and increasing in 5 dB steps. The participants were instructed to indicate till what level of noise, they could hear the speech clearly (background noise level [BNL]). ANLs were calculated as the difference between MCL and BNL (in dB). This was executed using a custom-made graphical user interface designed in MATLAB 2012 (Mathworks, Natick USA).
Procedure of Concurrent Vowel Identification (CCV) test
In this test, vowel ǀaǀ was presented concurrently with one among the vowels ǀeǀ,ǀuǀ,ǀoǀ, or ǀiǀ in random order. There were 20 stimulus presentations comprising 5 presentations per each pair. The stimuli were presented at MCL, and the participants were instructed to click the vowel that occurred along with ǀaǀ in each trial. This was a closed set task with aforementioned 4 vowels. Score of each participant out of 20 was taken. This was executed using a custom-made graphical user interface designed in MATLAB 2012 (Mathworks, Natick USA).
Operation SPAN Test
In this test, a series of equations were presented on a computer screen, which the participant had to solve aloud and indicate if it was right or wrong. Below each equation, a letter was displayed, and the participants were asked to memorize the letters along with the task of solving the equation. Two to six such pairs of equations and the corresponding letter were presented, and the participants had to memorize the letters and repeat all the letters which appeared in that particular set in the correct sequence. There were 12 such sets. The responses were scored as recommended by Conway et al.
Backward Digit Span
In this test, a series of numbers were presented to the participants. The participants had to listen to several series (2–12) of numbers and repeat them back in the reverse sequence. The longest sequence of numbers repeated back in the correct sequence was counted as the digit span of the participant.
All the test procedures were carried out in a quiet room using a Dell laptop and Sennheiser HDA200 circumaural headphones. The stimuli were presented to the ear preferred by the participants.
| Results|| |
The individual data were tabulated in Statistical Package for the Social Sciences (Version 17, SPSS Inc., Chicago). [Figure 1] gives the mean and standard deviation of SNR-50, CCV, ANL, and OSPAN in the three groups of participants. From the table, it can be observed that mean values were highest in the Grad group followed by SQ group and were least in the NES group. This was true for SNR-50, CCV, ANL, and OSPAN tests. On the other hand, the mean digit span scores were highest for Grad group while the scores were comparable in the SQ and NES groups.
|Figure 1: Means and standard deviations of the scores of the three groups in the different tests used. Error bars indicate ± 1 standard deviation|
Click here to view
Multivariate analysis of variance
The group data were statistically analyzed using multivariate analysis of variance (MANOVA) in SPSS platform. The results of MANOVA showed a significant main effect of group on the data (Wilks lambda F(10,132) =2.866, P < 0.01, η2 = 0.165). The individual univariate ANOVAs showed that there was a significant main effect of group on CCV (F (2,71) = 5.818, P < 0.01), SNR-50 (F (2,71) = 4.087, P < 0.05) and OSPAN (F (2,71) = 7.0007, P < 0.001). However, there was no significant main effect of group on ANL (F (2,71) = 1.060, P > 0.05) and Backward Digit Span (F (2,71) = 2.285, P > 0.05). Because there was a significant main effect of group, the data were further subjected to Bonferroni's pair-wise comparisons. The results of the pair-wise comparison showed that there were significant differences in scores of CCV, SNR-50, and OSPAN between NES and Grad group. However, there was no significant difference between NES and SQ in any of the tests.
Discriminant function analysis
Canonical discriminant function analysis was used to assess as to which of the dependent variables measured, could significantly classify the data into the predetermined three groups. Results of analysis showed that CCV (F (2,72) = 6.285, P = 0.003), SNR-50 (F (2,72) = 4.411, P = 0.016), and OSPAN (F (2,72) =10.099, P = 0.000) emerged as significant classifiers while ANL (F (2,72) = 1.070, P = 0.342) and digit span (F (2,72) = 0.453, P = 0.638) were not significant classifiers. [Figure 3] shows results of the discriminant function analysis. It shows the canonical score plot of 2 discriminant functions with maximum eigenvalues and the grouping of the data.
|Figure 2: Scatter plots of the psychoacoustic and working memory measures pooled across the three groups|
Click here to view
|Figure 3: Canonical scores plotted across the two discriminant functions with the highest Eigenvalues. The ellipses show the 95% confidence boundaries of each group|
Click here to view
Since there was a significant difference between the groups on the auditory and cognitive variables tested in the study, we were interested to know the relationship among the various dependent variables (auditory as well as cognitive). This would enable us to understand whether the effect of noise on one variable is likely to inform about the effect of noise on the other variable or not. For example, if the measures of auditory memory and SPIN are correlated, it would mean that if noise affects the auditory memory, it would be likely to influence SPIN as well. Pearson product-moment correlation was used for this purpose. The results of correlation revealed a significant correlation between CCV and SNR-50 (r = −0.325, P < 0.05), SNR-50 and digit span (r = −0.239, P < 0.05), and digit span and OSPAN (r = 0.289, P < 0.05). [Figure 2] shows the scatter plots for different pairs of variables. The plots show that, as CCV increased, SNR-50 decreased and vice versa. However, a detailed inspection of the scatter plots of other two pairs of variables shows that the obtained relationship is due to an outlier. On removing this outlier, the correlation coefficient changed to r = −0.241 (P < 0.039) for SNR-50 and digit span and similarly for digit span and OSPAN coefficient changed to r = 0.307 (P < 0.0008).
To check if the effect of environmental noise exposure on the SNR-50 was mediated by an effect on the working memory, mediation analysis was carried out. Mediation analysis was carried out using a linear regression approach. The groups with differing levels of environmental noise exposure served as the categorical independent variable. The working memory in terms of the Backward Digit Span served as the second independent variable and served as the mediator variable to be tested. SNR-50 was considered as the dependent variable. The raw regression coefficients and standard errors for the group predicting the working memory was computed, followed by the group and working memory predicting SNR-50 in combination. These raw regression coefficients and standard errors were then subjected to the Aroian test to test the null hypothesis that the working memory did not significantly mediate the effect of noise exposure on SNR-50. The result showed that the null hypothesis was true (Aroian test statistic = 0.268, standard error = 0.116, P > 0.05). This suggests that working memory was not a significant mediator and the noise exposure had a direct effect on working memory and SNR-50.
| Discussion|| |
The present study aimed to test the null hypothesis that there is no significant difference between individuals with and without environmental noise exposure in their SPIN, stream segregation abilities, and the working memory abilities. The results of the present study did not support the null hypothesis as there were differences in CCV, SNR-50, and OSPAN between experimental group and control group 2. There was no difference across the three groups in the noise tolerance and working memory as tested on Backward Digit Span.
Concurrent vowel identification tests the individual's ability of sound stream segregation, SNR-50 assesses SPIN, and OSPAN assesses working memory. Mean data showed that participants in the experimental group performed poorer in all these skills compared to control group 2. Participants in the experimental group were exposed to environmental noise in a busy market daily for >10 h while participants in the control group did not have such an exposure. Therefore, poorer performance could be attributed to environmental noise exposure. The Leq of noise in the peak hours was 76.5 dBleq, which is well below the DRC. Therefore, it can be inferred that even the noise levels below DRC can have negative effects on the higher auditory functioning. It can be seen that the shopkeepers in relatively quieter environments had slightly poorer abilities (not statistically significant) when compared to the control group 2. This possibly represents a continuum of deterioration in perceptual abilities due to levels of noise exposure. The present finding is in consensus with the reports in animals by Zhou and Merzenich.
Zhou and Merzenich  exposed adult rats to structured noise at a sound pressure level of 65 dB. Results showed that there were substantial negative consequences for the auditory system documented at the cortical level, attributable to environmental exposure. This was true in spite of noise having no impact on hearing sensitivity. These effects were present even with 10-h daily exposure. Therefore, it can be concluded that even the environmental noise levels below DRC are hazardous to higher auditory, and cognitive functions such as SPIN. Stream segregation and working memory abilities. The present study is a preliminary finding and the first report on the effects of environmental noise exposure in adult humans and future research should dwell lot more into its intricacies. It gives a new direction to the future studies wherein other aspects of central auditory processing can be probed in individuals with regular environmental noise exposure. The present study differs from studies on effects of environmental noise exposure as this was done in adults, while the previous ones were done in children during their developmental period.
The findings of the present study are also similar to that obtained in Kumar et al.'s studies ,, in humans. However, in their study, individuals were exposed to noise levels above DRC, whereas in the present study, the exposure levels were below DRC.
Interestingly, negative influence was observed not just on the higher auditory functions but also on working memory. Working memory is a cognitive skill and is important for perception in challenging conditions. The present finding indicates that continuous long hours of environmental noise exposure lead to both auditory and nonauditory dysfunctions.
During the conceptualization of the present study, it was speculated that shopkeepers in the busy markets may possess better SPIN as they are accustomed to understanding speech in noise on a daily basis. Considering that subcortical and cortical structures are malleable,,, it was speculated that regular noise exposure serves a function similar to noise desensitization training and thereby shall lead to better SPIN over the years. For instance, many training-based studies ,, have reported that training facilitates neural plasticity and enhances afferent and efferent auditory functioning. However, the current results showed a paradoxical finding. This shows that the auditory system treats noise and signal (speech or music) in a paradoxical way. While exposure to music has positive influence on the auditory system, exposure to noise has negative influence, even when the levels do not reach DRC.
Furthermore, the present study showed a relationship between SPIN and sound stream segregation abilities. That is, better stream segregation abilities were translated into better SPIN. Therefore, it can be inferred that poorer SPIN found in experimental group can be partly attributed to the poorer stream segregation consequent to environmental noise exposure. Therefore, one can predict that if environmental noise negatively affects stream segregation skills and digit span, it is likely to reduce speech perception.
Similarly, there was a significant correlation between SNR-50 and digit span as well as digit span and OSPAN. This shows that there is a relationship between working memory and SPIN. The finding is supported by reports in the literature.,
Discriminant function analysis aided in reducing the dimensionality of the data to explain and to identify the target measures which best explained the data. It showed that CCV, SNR-50, and OSPAN served as classifiers and helped in differentiating the three groups of participants in terms of their psychoacoustic and cognitive abilities. This suggests that there is a possible strong relation between the environmental noise exposure and the auditory-cognitive deficits found between the groups as this reverse validated the grouping of the participants in the study.
Mediation analysis interestingly revealed that the effects of environmental noise exposure on SPIN were not mediated by working memory. This suggests that exposure to environmental noise had a direct effect on SPIN rather than affecting working memory which then influences SPIN.
The results of the study are preliminary in nature and should be generalized with caution, due to the small sample size of the study. The small sample size in the study was a result of attempting to control for the noise levels that each group was exposed to, as the number of shopkeepers per area, especially the residential area was rather limited.
| Conclusions|| |
Regular exposure to environmental noise even if the levels are below DRC will have negative influence on speech perception, stream segregation skills, and working memory abilities. Audiologists should have this theoretical knowledge to educate the public to prevent further damage.
We thank the All India Institute of Speech and Hearing for the resources provided to carry out the study. We are also thankful to all the participants who volunteered to actively participate in the study. We thank Mr. Sreeraj K. for providing the information about noise levels from his noise level survey data.
Financial support and sponsorship
GNG was funded by the AIISH Research Fellowship during the course of this research work.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Kujawa SG, Liberman MC. Adding insult to injury: Cochlear nerve degeneration after “temporary” noise-induced hearing loss. J Neurosci 2009;29:14077-85.
Noreña AJ, Gourévitch B, Aizawa N, Eggermont JJ. Spectrally enhanced acoustic environment disrupts frequency representation in cat auditory cortex. Nat Neurosci 2006;9:932-9.
Kumar UA, Ameenudin S, Sangamanatha AV. Temporal and speech processing skills in normal hearing individuals exposed to occupational noise. Noise Health 2012;14:100-5. [Full text]
Ganesan D, Kumar AU. Effect of Personal Music Systems on Hearing in Young Adults. In: Student Research at AIISH, All India Institute of Speech and Hearing, Mysuru; 2012. p. 79-88.
Bregman AS. Auditory Scene Analysis: The Perceptual Organization of Sound. Cambridge, Mass: A Bradford Book; 1994. p. 792.
Pressnitzer D, Sayles M, Micheyl C, Winter IM. Perceptual organization of sound begins in the auditory periphery. Curr Biol 2008;18:1124-8.
Chandrasekaran B, Kraus N. The scalp-recorded brainstem response to speech: Neural origins and plasticity. Psychophysiology 2010;47:236-46.
Kumar UA, Vanaja CS. Functioning of olivocochlear bundle and speech perception in noise. Ear Hear 2004;25:142-6.
Maruthy S, Kumar AU, Gnanateja GN. Online Modulatory Mechanisms and Speech Perception in Noise: A Probe Through Auditory Far-Field Responses. ISHACON 45; 2013.
Wong PC, Uppunda AK, Parrish TB, Dhar S. Cortical mechanisms of speech perception in noise. J Speech Lang Hear Res 2008;51:1026-41.
Ameenuddin M, Maruthy S. Effect of Music on Neural Plasticity of Efferent Auditory System. Dissertation Sunbmitted to the University of Mysore, Mysore; 2010.
Madhok P, Maruthy S. Neurophysiological Consequences of Auditory Training on Sub-Cortical and Cortical Structures. Dissertation Sunbmitted to the University of Mysore, Mysore; 2011.
Szalma JL, Hancock PA. Noise effects on human performance: A meta-analytic synthesis. Psychol Bull 2011;137:682-707.
Hygge S, Evans GW, Bullinger M. A prospective study of some effects of aircraft noise on cognitive performance in schoolchildren. Psychol Sci 2002;13:469-74.
Stansfeld S, Hygge S, Clark C, Alfred T. Night time aircraft noise exposure and children's cognitive performance. Noise Health 2010;12:255-62.
] [Full text]
Francis AL, Nusbaum HC. Effects of intelligibility on working memory demand for speech perception. Atten Percept Psychophys 2009;71:1360-74.
Rudner M, Lunner T, Behrens T, Thorén ES, Rönnberg J. Working memory capacity may influence perceived effort during aided speech recognition in noise. J Am Acad Audiol 2012;23:577-89.
Baddeley A. Working memory: Theories, models, and controversies. Annu Rev Psychol 2012;63:1-29.
Baddeley A. Working memory: Looking back and looking forward. Nat Rev Neurosci 2003;4:829-39.
Matsui T, Stansfeld S, Haines M, Head J. Children's cognition and aircraft noise exposure at home – The West London schools study. Noise Health 2004;7:49-58.
] [Full text]
Zhou X, Merzenich MM. Environmental noise exposure degrades normal listening processes. Nat Commun 2012;3:843.
Mohammed H, Kumar AU. Effect of Musical Training on Psychoacoustic Measures. Dissertation Sunbmitted to the University of Mysore, Mysore; 2013.
Nabelek AK, Freyaldenhoven MC, Tampas JW, Burchfiel SB, Muenchen RA. Acceptable noise level as a predictor of hearing aid use. J Am Acad Audiol 2006;17:626-39.
Conway AR, Kane MJ, Bunting MF, Hambrick DZ, Wilhelm O, Engle RW, et al.
Working memory span tasks: A methodological review and user's guide. Psychon Bull Rev 2005;12:769-86.
Song JH, Skoe E, Banai K, Kraus N. Training to improve hearing speech in noise: Biological mechanisms. Cereb Cortex 2012;22:1180-90.
Song JH, Skoe E, Banai K, Kraus N. Perception of speech in noise: Neural correlates. J Cogn Neurosci 2011;23:2268-79.
[Figure 1], [Figure 2], [Figure 3]