Cognitive and Electrophysiologic Correlates of Listening in Noise

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Authors Everett, Alyssa J.

Publisher The University of Arizona.

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COGNITIVE AND ELECTROPHYSIOLOGIC CORRELATES OF LISTENING IN NOISE

Alyssa Everett

Audiology Doctoral Project Submitted to the Faculty of the

DEPARTMENT OF SPEECH, LANGUAGE, AND HEARING SCIENCES

In Partial Fulfillment of the Requirements

For the Degree of

DOCTOR OF AUDIOLOGY

In the Graduate College

THE UNIVERSITY OF ARIZONA

2018 THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE

As members of the Audiology Doctoral Project Committee, we certify that we have read the project prepared by Alyssa Everett, titled Cognitive and Electrophysiologic Correlates of Listening in Noise and recommend that it be accepted as fulfilling the Audiology Doctoral Project requirement for the Degree of Doctor of Audiology.

______Date: ______Barbara Cone, Ph.D., CCC-A

______Date: ______Nicole Marrone, Ph.D., CCC-A

______Date: ______Frank Musiek, Ph.D., CCC-A

______Date: ______David Velenovsky, Ph.D., CCC-A

______Date: ______Stephanie Griffin, Ph.D., CIH

Final approval and acceptance of this project is contingent upon the candidate’s submission of the final copies to the Graduate College.

I hereby certify that I have read this Audiology Doctoral Project prepared under my direction and recommend that it be accepted as fulfilling the project requirement.

______Date: ______Barbara Cone, Ph.D., CCC-A 3

STATEMENT BY AUTHOR

This Audiology Doctoral Project has been submitted in partial fulfillment of the requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library.

Brief quotations from this Audiology Doctoral Project are allowable without special permission, provided that an accurate acknowledgement of the source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author.

SIGNED: Alyssa Everett

ACKNOWLEDGEMENTS

After two years of working rigorously on this research, I am proud to present my project and acknowledge those who have supported me every step of the way. This endeavor has not only allowed me to grow as a researcher and a professional in the field of audiology, but also as an individual. Personally, I have gained the invaluable skills of motivation and dedication. Upon completion of this project, I have taken the time to reflect and value the team who has supported me from the beginning.

First and foremost, I would not have been able to complete my project without recognizing my committee members. None of this could have been done without the fabulous guidance of my primary mentor, Dr. Barbara Cone. She gave me the helpful pushes I needed, even when I thought I would not be able to continue. Dr. Cone, you were the best support I could have asked for, especially in time of methodological trouble, and for that, I truly thank you. It is also important to acknowledge my other committee members, Drs. Nicole Marrone, Frank

Musiek, David Velenovsky, and Stephanie Griffin. With their added encouragement and feedback, we were all able to see my work come to fruition.

Finally, I truly would not have been able to complete my research without the unwavering confidence of my two biggest fans: my mother, Elena and my brother, Ryan. From the bottom of my heart, mind, and soul, thank you. You have picked me up from my lowest points and raised me higher than I ever thought possible. Thank you for your never-ending encouragement, excitement, ears to listen to my troubles, and steadfast faith. Your continuing support and love through the good and the bad times means the world to me.

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...... 4 TABLE OF CONTENTS ...... 5 LIST OF FIGURES ...... 6 LIST OF TABLES ...... 7 ABSTRACT ...... 8 INTRODUCTION AND REVIEW OF THE LITERATURE...... 9 Effect of Cognitive Load on the ANL ...... 12 Electrophysiology of ANL ...... 13 Effects of the Stimulus on CAEPs ...... 18 Statement of Purpose ...... 19 METHODS AND MATERIALS ...... 20 Experiment 1 ...... 20 Participants...... 20 ANL stimuli and calibration...... 20 CAEP stimuli and calibration...... 21 Procedure for acceptable noise level testing...... 21 Behavioral cognitive load task...... 22 CAEP tests...... 23 CAEP 2-chirp test...... 23 Experiment 2 ...... 23 Participants...... 23 Procedure for acceptable noise level testing...... 24 Behavioral cognitive load task...... 24 Statistical analyses...... 24 RESULTS ...... 24 Experiment 1 ...... 24 Experiment 2 ...... 30 Comparison of Results from Lateralized vs. Binaural Test Conditions...... 31 Correlation of ANL and CAEP Data ...... 32 DISCUSSION ...... 34 Electrophysiology ...... 37 Conclusions and Clinical Implications ...... 38 APPENDIX A – DATA COLLECTION INSTRUMENTS ...... 41 APPENDIX B – ADMINISTERING THE ACCEPTABLE NOISE LEVEL TEST ...... 42 REFERENCES ...... 43

LIST OF FIGURES

Figure 1...... 25 Figure 2...... 26 Figure 3...... 26 Figure 4...... 28 Figure 5...... 29 Figure 6...... 31 Figure 7...... 32

LIST OF TABLES

Table 1...... 29-30 Table 2...... 33 Table 3...... 33

ABSTRACT

People with hearing loss experience many challenges when adjusting to using amplification (hearing aids). An almost universal complaint is difficulty hearing in the presence of noise. The Acceptable Noise Level (ANL) test is used to estimate a person’s tolerance for listening with noise present. Better tolerance is associated with greater success with amplification, as measured by duration and consistency of use. Yet, there are unknowns about why this should be so. One question is whether the ANL is related to the brain’s ability to inhibit or suppress noise. Another is whether ANL is a stable value or if it changes when challenging cognitive tasks are performed. Two experiments in this study analyze the effects of a cognitive load on unilaterally and bilaterally presented ANL scores and their relationship to the sensory gating mechanism measured through cortical auditory evoked potentials (CAEP). Results indicated a significant increase in the ANL score measured with the addition of a cognitive load in the unilateral and bilateral condition. A number of statistical analyses were performed to assess differences between measurements of CAEPs and behavioral results and none were statistically significant. 9

INTRODUCTION AND REVIEW OF THE LITERATURE

Noise is ubiquitous in a person’s daily life. Background noise can cause a person stress

(Westman & Walters, 1981), increase their cognitive load (Wendt, Dau, & Hjortkjær, 2016) and be a distraction (Cohen, Evans, Krantz, & Stokols, 1980; Cohen, Krantz, Evans, & Stokols,

1980). Cognitive load is considered to be the total amount of mental effort being used in working memory, both short and long term (Sweller, 1988). As it relates to audiology and hearing aid use, noise is typically associated with sounds that are unpleasant or distracting, can come from any direction and mix with the speech signal to reduce speech perception or tolerance for sound

(Wong, Uppunda, Parrish, & Dhar, 2008; Medwetsky, 2009; Li & Jeng, 2011). Hearing aid users often report background noise as being a high priority problem in their daily lives (Hickson,

Clutterbuck, & Khan, 2010). When a person experiences intolerable noise with hearing aid usage, they become less likely to use their device due to the negative experience (Palmer,

Bentler, & Mueller, 2006). Currently, there are no standardized hearing aid measures used to evaluate whether a person will have negative experiences as a result of background noise. The

Acceptable Noise Level (ANL) test, developed by Nabelek, Tucker, and Letowski (1991) has been investigated as a method to predict a hearing aid user’s use of amplification, based on their tolerance of noise.

The ANL measures the self-reported tolerance of the listener for noise (an 8-talker babble) mixed with a speech signal. This tolerance level appears to be positively correlated to more consistent hearing aid use, so that the person actually benefits from the amplified sound produced by the hearing aid (Nabelek, Freyaldennhoven, Tampas, Burchfield, & Muenchen,

2006). The ANL test reports performance on a numerical scale with a lower number representing better tolerance of noise and a higher number representing poorer tolerance of noise. 10

Specifically, listeners with scores less than 7dB are more likely to wear their hearing aids regularly, whereas listeners that score 13dB or greater are more likely to be part-time or non- users of their hearing aids (Nabelek et al., 2006). Nabelek et al. (2006) indicated that these predictions of hearing aid usage were found with 85% accuracy. However, there is a large variation and standard deviation range across studies that evaluated ANL with no explanation of this large range.

Nabelek et al. (1991) suggested that an acceptable noise level score would assist the audiologist in terms of hearing aid selection and as a counseling tool. Specifically, the ANL may provide the audiologist with information about the individual’s ability to tolerate noise despite possibly doing well on speech understanding tasks. Difficulty listening in background noise is a common complaint from hearing aid users and can impact the consistency of how often the devices are worn (Kochkin, 2002). Although speech-in-noise tests can be completed as part of a hearing aid evaluation, these tests measure how well a person can recognize words, given a certain level of background noise. There is not a clear relationship between performance on these tests and actual hearing aid use (Bentler, Niebuhr, Getta, & Anderson, 1993; Humes, Halling &

Coughlin, 1996). In contrast, the ANL test, has been reported to correlate well with an individual’s actual usage of the device. Information obtained from the ANL test can be used to counsel the wearer on realistic expectations. For example, in situations with a lot of background noise and a high ANL score, they will likely experience less tolerance to speech in noisy environments. Specifically, knowing ahead of time that a listener has poor tolerance of noise, hearing aids with greater noise reduction capabilities, more directionality, and/or assistive devices may be needed (Franklin, Johnson, White, Franklin, & Smith-Olinde, 2013). 11

Acceptable noise levels are thought to be dependent on “inherent” psychological factors and not acoustic variables such as degree of hearing loss or even prior use of a hearing aid

(Nabelek et al., 1991; Nabelek, Tampas, & Burchfield, 2004). There are additional factors that have been shown to have no effect on the ANL whereas other factors show an effect, which may help to explain the variation in the ANL literature. Nabelek, Tampas, and Burchfield (2004) determined that acceptable noise levels are unaffected by amplification and are inherent to the patient. In addition, Nabelek et al. (1991) tested 75 people and determined that ANL is independent of the degree of hearing impairment. Additionally, other studies have evaluated the effect of age (Moore, Gordon-Hickey, & Jones, 2011), gender of the listener (Rogers, Harkrider,

Burchfield, & Nabelek, 2003), cochlear implant usage (Plyler, Bahng, & Hapsburg, 2008), and the listener’s music preference (Gordon-Hickey & Moore, 2007) and also found no significant effect on the ANL score.

Although the amount of noise that is tolerated appears inherent to the listener, there are factors that have been shown to affect the ANL score, some of which are contradictory with other studies. Gender of the speaker (Gordon-Hickey, Moore, & Estis, 2012), speech presentation level (Franklin, Thelin, Nabelek, & Burchfield, 2006), hyperacusis (Levy, Peck, &

Balachandran, 2011), hearing loss (Fredelake, Holube, Schlueter, & Hansen, 2012), and hearing aid usage (Ahlstron, Horwitz, & Dubno, 2009) are all factors that have been evaluated and shown to affect the listener’s ANL score. Based on findings in the literature, there is no consistent agreement on whether hearing loss or aided vs. unaided conditions affect ANL.

Differences in experimental design may be leading to conflicting results in what affects

ANL. For example, Nabelek et al. (2004) and Ahlstrom et al. (2009) found conflicting results regarding the effects of hearing aid use on a person’s ANL. However, there were differences in 12 their study design. Nabelek included 41 participants while Ahlstrom only had 21, Nabelek utilized a 1-3-month acclimatization period while Ahlstrom used a 3-6-month period. This is an important difference because one of Nabelek’s conclusions was that there was a lack of change over the 3-month test period. Additionally, Ahlstrom found changes in ANL related to hearing loss when the researchers incorporated spatially separated noise, playing the noise separate from the sentences at 90 degrees azimuth, whereas Nabalek used the conventional methods for determining ANL, with co-located speech and noise.

Nabelek et al. (1991) and Fredelake et al. (2012) also found conflicting results regarding the effects of hearing loss on ANL scores. Their contradiction was in reference to hearing loss and also may be a result of experimental design. Nabelek and colleagues incorporated 45 hearing impaired and 15 normal hearing individuals into their study in which the ANL test was completed in the conventional way, with speech and noise presented under headphones.

Fredelake et al. on the other hand, had 10 normal-hearing and 11 hearing-impaired participants responding to conditions in which noise reduction algorithms had been applied to the stimuli.

These researchers found that there was an increased acceptance of noise (or lower ANL scores) when the noise reduction algorithms were applied, a finding only significant in the hearing loss group. In addition to this difference in methodology, Fredelake et al. concluded that there were large inter-individual differences. The combination of these differences likely explains why there are conflicting results for effects of hearing loss and amplification on the ANL test.

Effect of Cognitive Load on the ANL A factor that has not received systematic investigation is the effect of cognitive load on the ANL. A cognitive load adds additional demands to the processing centers of the brain which may interfere with learning and understanding. Although there are multiple working memory 13 models (Cowan, 1999, 1999), in the current investigation, the Baddeley and Hitch model from

1974 is adopted. This model specifies three components that comprise working memory: The

Phonological Loop (Language), the Visuospatial Sketchpad (Visual), and the Episodic Buffer.

The Phonological Loop represents the content of verbal information stored in cognition and the

Visuospatial Sketchpad relates to the spatial relationship and perception of objects seen visually but both contribute to short-term memory (Baddeley, 1966; Baddeley, 1997).

Verbal working memory is included in the Phonological Loop and entails verbally articulating tasks while the Visuospatial Sketchpad entails seeing information and storing it to memory, such as a string of numbers. The Episodic Buffer describes the “backup” system in our long-term memory which allows for executive control and linking of information that also influences our working memory (Baddeley, 2000). Different factors such as attention and stress can affect working memory tasks, and in this study, we will evaluate how the introduction of multi-talker babble, such as the kind used to determine ANL, affects working memory. Previous research has shown that noise increases the number of errors in a working memory task (Hockey,

2013) and that noise affects cognitive load (Kirschner, 2002).

Electrophysiology of ANL There has also been limited study of the neurophysiologic underpinnings of the ANL using electrophysiologic measures. Tampas and Harkrider (2006) were the first to investigate the electrophysiological correlates of the acceptable noise level test using auditory evoked potentials

(AEPs), specifically, the auditory brainstem responses (ABR), auditory middle latency response

(AMLR), and “late latency responses” also known as cortical auditory evoked potentials

(CAEP). They tested 21 female subjects with normal hearing and split them into two groups based on their ANL score. The first group included 11 participants with low ANL (6dB or less) 14 and the second group included 10 participants with high ANL (16dB or more). The evoked responses were obtained in response to Blackman-gated tone-bursts presented in quiet and during passive listening. They demonstrated that those who had high ANL scores, indicating low tolerance of background noise, had larger AMLR and CAEP amplitudes compared to those who had lower ANL scores and therefore presumably, better tolerance of noise. Tampas and

Harkrider discussed these findings with respect to cortical inhibition in that, the physiologic activity differs depending on if the participants had a high or low ANL score. Cortical inhibition can be simply thought of as the brain’s ability to reduce the amount of activation from a sound source, in this case, noise. Tampas and Harkrider interpreted their findings as indicating that the amount of binaural processing above the superior olivary complex these individuals had may have differed depending on their behavioral outcomes. The author’s conclusions suggest that those who have a lower (better) ANL score also have a greater cortical inhibition, or suppression of noise, although they did not test this hypothesis.

Brännström et al. (2012) aimed to replicate and extend the work of Tampas and Harkrider

(2006) by introducing a cognitive task into the protocol. Auditory evoked potentials were acquired in a similar fashion as Tampas and Harkridrer, in that, ABR, MLR, and LLRs were recorded using tone-bursts. The cognitive task added to the behavioral methodology included a general working memory task of the Swedish Reading Span Test. This test utilizes sentences that are either semantically acceptable or unacceptable. The participant is simply asked to report if the sentence they heard was semantically acceptable. Following this task, they were asked to recall the first word in each of the sentences presented and performance accuracy was scored based on the number of words correctly remembered. Brännström and colleagues were not able to replicate the Tampas and Harkrider findings of lower AMLR and CAEP amplitudes for those 15 with low ANL scores, nor did they find a correlation between their cognitive task and CAEP components. However, they established a positive correlation between working memory capacity and the tolerance of background noise score. Specifically, the researchers suggested that a person’s maximum tolerance of background noise (determined from the ANL) would positively correlate to their working memory capacity as measured by the reading span task. These researchers found that those with higher working memory capabilities had increased tolerance for background noise, but only a weak correlation was determined (Brännström et al., 2012).

Another mechanism that may underlie the ability to tolerate background noise is sensory gating. This mechanism is one way that the auditory nervous system gives precedence of one stimulus by suppressing the response of a second (Cheng et al., 2016). This suppression is the result of the brain filtering out irrelevant input. In classic auditory electrophysiology, a “two- click” stimulus paradigm is used to document sensory gating. In this paradigm, two clicks are presented with a 500 ms inter-stimulus interval, with a 3-5 second interval between each presentation of the click pair. Responses are averaged for each click pair. Measuring the responses of sensory gating can be accomplished using P50 of AMLR or P1 of CAEP (Picton,

2011). The amplitude in response to the second stimulus is measured with respect to the first stimulus and expressed as an amplitude ratio. Lower ratios indicate better suppression of irrelevant information and high ratios, the opposite (Boutros, Belger, Campbell, D’Souza, &

Krystal, 1999). Specifically, Waldo and Freedman (1983) indicated that people with normal suppression show a 75.4% decrease in the response to the second stimulus compared to the first on the P1-N1-P2 complex of cortical auditory evoked potentials.

Factors that may affect sensory gating have been documented in the literature. Waldo and

Freedman (1983) concluded that motor activity, distraction/concentration, and altered mood do 16 not significantly affect the suppression mechanism. Although, the authors did note that those with psychosis or mental disorders do show abnormal sensory gating. Because of this, the sensory gating mechanism has been extensively studied for patients who have schizophrenia

(Picton, 2011). One study concluded no significant latency differences between individuals with and without schizophrenia. However, there was a statistically significant difference in the amplitude, in that the group with schizophrenia had a larger amplitude to the second stimulus compared to the first stimulus (Clementz, Geyer, & Braff, 1998). Additional studies concluded the same findings in that sensory gating mechanism is reduced in schizophrenia (Freedman et al,

1987; Nagamoto, Adler, Waldo & Freedman, 1989; Waldo, et al., 1992). Other studies have shown a reduced suppression in those with Alzheimer’s disease (Buchwald, Erwin, Rea, van

Lancker, & Cummings, 1989), post-traumatic stress disorders (Ghisolfi et al., 2004), and bipolar affective disorders (Lijffijt et al., 2009). There are no current studies on the effect of stimulus plus noise on the electrophysiological sensory gating mechanism to the author’s knowledge and thorough literature review.

Cortical auditory evoked potentials have been and are currently used to evaluate mechanisms underlying perceptual processes, such as speech in noise perceptual abilities, evidence of learning disabilities, and central auditory processing disorders (CAPD). Anderson,

Chandrasekaran, Yi, and Kraus (2010) evaluated the CAEPs evoked by speech tokens in 32 children, aged 8-13 years. The children were separated into two groups, those with high performance on the Hearing in Noise Test (M=-4.49dB SNR) and those with low scores (M=-

2.3dB SNR). The top performers had significantly smaller N2 amplitudes in the presence of noise compared to the bottom performers, although there was no difference between the groups in the no-noise condition. Anderson, Chandrasekaran and Kraus (2010) suggested that these 17 findings may reflect “greater neural efficiency” in the top performers on the HINT as a result of fewer neural resources being recruited. Another study analyzed the cortical components of auditory processing among children who had a diagnosis of a learning disability as compared to a control group without a history of learning disability (Purdy, Kelly, & Davies, 2002). In an attempt to control for auditory processing, the SCAN was administered, and results indicated that there was no significant difference in performance between groups. The results from this study showed that children identified as having learning disabilities had shorter latencies and smaller amplitudes for P1 and N1 and larger P2-N2 amplitudes as compared to children without a learning disability (Purdy et al., 2002). These studies provide objective measurements for children with speech in noise difficulties and for children who may have a learning disability.

Additional studies have examined the relationship between CAEPs and CAPD. Jirsa and

Clontz (1990) showed that children identified with auditory processing disorders have increased latencies for components N1, P2, and P3 as compared to a control group of children that were matched for age, intelligence, and gender. These authors suggested that CAEPs are potentially useful in the evaluation process of determining possible auditory processing disorders. Sharma,

Purdy, and Kelly (2014) added to the current literature on CAPD by evaluating 90 participants with suspected processing disorder and 22 children that had typical auditory processing abilities as measured behaviorally. All children were given an auditory processing test battery and those that scored at 2 standard deviations below the mean on one or more tests in the battery were designated to be in the CAPD group. Fifty of 90 children who were suspected of CAPD met the aforementioned criterion. Sharma et al. found that cortical component P1 was significantly smaller in the CAPD group, suggesting “poorer sound representation at the level of the auditory cortex.” 18

A comprehensive review of central auditory processing disorders was evaluated in a recent article (Musiek, Shinn, Chermak, & Bamiou, 2017). This review highlighted research that analyzed the relationship between CAPD and CAEPs and summarized the findings as: 1) N1-P2 amplitudes approximate the behavioral gap detection threshold (Palmer & Musiek, 2013), 2) replicable cortical evoked potentials reflect “good” or functional neural substrate of the auditory cortex (Musiek, Chermak, & Cone, in press), and 3) P1-N1-P2 may be abnormal or absent with lesions of the primary auditory cortex (Knight, Hillyard, Woods, & Neville, 1980).

Effects of the Stimulus on CAEPs In the literature, cortical evoked potentials have been acquired using clicks, tone bursts, and speech stimuli. However, a chirp stimulus was utilized for the current study. The chirp is a stimulus that takes the basilar membrane response to stimuli into consideration. Clicks and other transient stimuli have a wide spectral spread that results in asynchrony on the basilar membrane, with a preference to high frequencies (Dau, Wegner, Mellert, & Kollmeier, 2000). However, the chirp stimulus is designed to attempt maximal synchrony on the cochlear partition and in turn, the 8th nerve with a rising frequency stimulation (Dau et al., 2000).

Chirps, or now referred to as rising frequency chirps, have been shown to generate greater wave V amplitudes in the ABR in adults and infants (Fobel & Dau, 2004; Elberling, &

Don, 2008; Elberling, Callo, & Don, 2010). Elberling, Callo, and Don (2010) went on to show that short sweep rate chirps are more effective at stimulation levels that are higher (60 dB nHL) and longer sweep rate chirps at lower stimulation levels (20 dB nHL). Atcherson and Moore

(2014) evaluated how the chirp fares in comparison to click and tone bursts at the level of the midbrain through the use of middle latency responses. The researchers found that using a chirp resulted in significantly larger Na-Pa and Pa-Nb amplitudes when compared to the other stimuli. 19

Although the chirp stimulus has been shown to be beneficial at the level of the brainstem and midbrain, there is little evidence that suggests how the chirp will be processed at the level of the cortex, in comparison to the click.

Statement of Purpose In this study, we aim to tie together the threads of cognitive load/working memory, auditory electrophysiology, and the Acceptable Noise Level test. The literature on acceptable noise levels and its correlation to hearing aid use does not account for the influence of cognitive load. We aim to measure the effects of a cognitive load on a person’s ANL (determined behaviorally) and measure the electrophysiological correlates among individuals with hearing thresholds within normal limits. The specific aims were to:

1) measure ANL with and without a cognitive load. Hypothesis: Introduction of a cognitive load will affect the ANL score in a negative way.

2) measure CAEP in a one-chirp paradigm as an estimate of sensory gating. Hypothesis:

Introduction of noise will have an effect on CAEP latency and amplitude, resulting in longer latencies and smaller amplitudes.

3) measure CAEP with and without a cognitive load. Hypothesis: Introduction of a cognitive load will have an effect on the cortical evoked potentials, resulting in longer latencies and larger amplitudes. It is hypothesized that longer latencies will be a result of increased processing loads and larger amplitudes due to reduced sensory gating.

4) To correlate the ANL measures with the CAEP measures. Hypothesis: Lower ANL scores will show more suppression on CAEPs.

In all of the published work to-date, ANL has been determined using bilateral presentation of speech and noise under headphones. This binaural condition is the typical method 20 for obtaining an ANL score and the speech and noise are considered to be co-located. It is the case that a methodological error occurred in the initial phase of data collection and that the ANL was determined with the speech presented to the right ear and background noise to the left ear.

This resulted in a lateralized or “spatially separated” condition. We were able to re-test 11/17 subjects using bilateral speech and noise for the ANL test. Thus, we have two types of ANL data, lateralized noise and bilateral noise.

METHODS AND MATERIALS

Experiment 1 Participants. Seventeen participants, 11 females and 6 males, with normal hearing sensitivity, defined by 2/3 responses at 20dB from 250-8000Hz, volunteered for the study and gave informed consent prior to any testing. They had a mean age of 25.9 years (range 22-32 years). All participants in this study used English as their primary spoken language, had normal otoscopic results, passed a bilateral pure tone screening results, and did not have a history of neurological disorders as determined by a questionnaire (See questionnaire provided in

Appendix A).

ANL stimuli and calibration. The ANL test stimuli used for this study were based on the original Nabelek et al. (1991) materials. The materials are the Arizona Travelogue passage

(Cosmos, Inc., Kelowna, British Columbia) read aloud by a male speaker as the main speech component and an eight-person multi-talker babble as the competing noise stimuli. The speech and background babble were played from a compact disc with the outputs routed by a Grason-

Stadler (GSI-61) audiometer with speech introduced to the right ear and multi-talker babble introduced to the left ear via insert earphones (Etymotic ER-3A). Speech and noise levels were calibrated using a Larsen-Davis 820 Sound Pressure Level (SPL) meter using a 2-cc coupler 21

(HA1) and a 1–inch condenser microphone. When routed through the audiometer, the speech signal at 60 dB HL was at 60 dBA SPL, and the babble at the same level was 61 dBA SPL.

CAEP stimuli and calibration. The test stimuli used for this study included the eight-person multi-talker babble from the original Nabelek et al. (1991) material and chirp stimuli. The background babble played from a sound file by a laptop computer routed to a Crown

D-75A Amplifier and MIXER ID then introduced to the left ear via insert earphone (Etymotic

ER-3A). The chirps were generated by the SmartEP Intelligent Hearing System (IHS) and routed to the right ear via insert earphone. Noise and chirp levels were calibrated using a Larsen-Davis

820 Sound Pressure Level (SPL) meter using a 2-cc coupler (HA1) and a 1-inch condenser microphone.

Procedure for acceptable noise level testing. The subjects were tested in a single walled sound-treated booth. The Arizona Travelogue was initially played to the right ear at 30 dB HL and adjusted based on the method of limits in 5 dB steps until the listener reported that the speech passage was too loud. Then, the level was adjusted down in 5 dB steps until the listener indicated that the passage was too soft. From here, the level was increased in 2.5-5 dB steps until the listener indicated that the passage was at their most comfortable listening level

(MCL). While the passage was still playing in the right ear, the multi-talker babble was introduced to the to the left ear at 5dB HL below the MCL and increased in 5 dB steps until the participant indicated that the speech was incomprehensible. Next, the babble was decreased in 5 dB steps until the participant indicated that the passage was clear and easy to understand. Finally, the babble was increased in 2.5-5 dB steps until the listener indicated that he/she could “listen to the story for a long period of time without getting tense or tired,” and this was considered his/her background noise level score or BNL. This sequence of measures was repeated 3 times 22 separately of each other and the results of each calculated ANl were averaged together. The ANL score corresponds to the listener’s MCL mins the BNL (ANL=MCL-BNL). (See instructions on how the ANL test was instructed to the participant, provided in Appendix B).

Behavioral cognitive load task. Our dual-task procedure consisted of the following.

Participants were shown 7 randomly selected numbers for 15 seconds and told to memorize them in the correct order. The numbers were displayed on a piece of paper font size 72 and given to the participant to memorize. A stop watch was used to time the 15 seconds, then the paper was taken away. Then, the participants were instructed to recite the alphabet backwards, skipping every 1, 2 or 3 letters, as fast as possible for two minutes. When a mistake was made, they were informed that they were incorrect and had to start over. Following the two-minute alphabet recitation task, the participant was asked to repeat the original 7 numbers. Performance was determined in two ways, first, by scoring the numbers correctly repeated, and second, by the number of start overs in the alphabet task.

There were three conditions in which this behavioral task was presented. First, the cognitive task was performed in quiet and scored based on the performance described above.

Then, the task was administered while presenting multi-talker babble in the participant’s left ear at the participant’s pre-determined BNL. Depending on the participant’s performance with noise at BNL, the background noise was decreased by 10 dB if the performance on the cognitive task decreased relative to performance in the quiet condition. If performance with background noise was equal or better than that in quiet, then the noise level was increased by 10 dB for the next trial. Once the participant reached the same or better number correct and the same or less number of start overs in the alphabet in noise, this level was determined to be their “New BNL”. 23

CAEP tests. CAEPs were obtained using the Intelligent Hearing Systems Smart-EP system. Responses to chirp stimuli were recorded with EEG filter settings of 1.0 Hz-30 Hz, and amplification of 50k. CAEPs were averaged over an epoch of 1,000 ms. Two-channel recordings were obtained with a non-inverting electrode at Cz, and inverting electrodes at each mastoid, with a 12dB octave roll off. The ground was placed on the forehead. Prior to recordings, electrode sites were scrubbed with NuPrep abrasive gel and electrodes were fastened with Ten20 conductive paste and tape. Impedances were all less than 5.0 Ohms throughout the testing. For each condition for every participant, P1-N1-P2 peaks were chosen separately by two of the authors. Following individual selection, peaks were agreed upon together.

There were two CAEP tests administered: the 1-chirp and 2-chirp tests. The stimulus for the 1-chirp test utilized a rate of 1/s. There were four conditions for the 1-chirp paradigm: 1) in quiet, 2) while counting out loud (for a motor control condition), 3) in quiet while doing the cognitive task described above for ANL, and 4) with the introduction of noise at the individual’s

BNL while performing the cognitive task.

CAEP 2-chirp test. The stimulus for this test consisted of two chirps with an inter- stimulus interval of 500ms. The two-chirp sequence was presented at a rate of 0.33 Hz. Three conditions were conducted using the 2-chirp paradigm: 1) in quiet, 2) at BNL, and 3) at New

BNL level.

Experiment 2 Participants. Eleven of the seventeen participants from experiment 1 were included in experiment 2. These 11 participants had a mean age of 25.3 years (range 22-31 years). 24

Procedure for acceptable noise level testing. Exactly like Experiment 1, the materials used to establish the ANL score were based on the Nabelek et al. (1991) methods.

However, the difference between Experiment 1 and 2 was the binaural or co-located condition.

Behavioral cognitive load task. The behavioral task and conditions used in

Experiment 2 were identical to the previous experiment except the noise used in the task was presented binaurally in Experiment 2 as compared to monaurally in Experiment 1.

Statistical analyses. Results were summarized in an Excel spreadsheet and were converted into StatView for descriptive and inferential statistics.

RESULTS

Experiment 1

ANL-speech right-noise left condition. The measurements made for the ANL tests were MCL, BNL and ANL. All ANL measurements were made first in the standard method and then in the condition of adding a cognitive load. Figure 1 provides a summary of the group mean values for each of those measurements. It is apparent that the addition of a cognitive load decreases the BNL from 69 dB HL to 47 dB HL, thus increasing the ANL from -16 dB HL to 6 dB HL, a 22 dB HL change. To test the first hypothesis that a cognitive load would affect the

ANL score, a paired comparison t-test was used to compare the difference between the original

BNL/ANL and the “new” BNL/New ANL, determined with a cognitive load. The New ANL was significantly higher than the original ANL, t9= 10.39, p < 0.001.

Figure 1. Results of behavioral tests. The mean values for unilateral MCL, BNL, and ANL determined without a cognitive load (purple bars) and with the addition of a cognitive load (red bars), and the difference between the BNL with and without a cognitive load (blue bar). Error bars indicate standard deviation.

CAEP component latencies and amplitudes were measured for each listening condition to test hypothesis 2. The mean latencies for each component and each condition are shown in

Figure 2. It is apparent from this figure that there was little effect of condition on CAEP component latency. This was tested using a repeated measures ANOVA. There were no statistically significant findings for the effect of condition on CAEP latency. Similarly, the mean

CAEP component amplitudes are graphed as a function of test condition in Figure 3. Although there was some variation in amplitude with condition, there was considerable inter-subject variability. Repeated measures ANOVA revealed no statistically significant difference in amplitudes as a function of test condition.

Figure 2. Results of 1 chirp paradigm latencies. No significant difference in latencies as a function of condition. Error bars indicate standard deviation.

Figure 3. Results of 1 chirp paradigm amplitudes. Mean amplitudes of each CAEP component are plotted as a function of listening condition. Error bars indicate standard deviation. 27

The third hypothesis was that noise would have an effect on the cortical evoked potentials

(latencies and amplitudes) obtained in a two-chirp sensory gating paradigm. The following latency criteria were applied and only those with components present within these latency windows were included in the analysis: P1: 45-90 ms, N1: 80-130ms, P2:150-230ms.

Additionally, all waveforms included in this analysis were morphologically appropriate. In

Figure 4, the difference in latency between the first and second stimulus is shown. It was expected that the latencies for the second chirp would be prolonged relative to the latencies obtained in response to the first chirp. The latency difference was determined from subtracting the latency in response to the first chirp from the latency in response to the second chirp. A positive difference indicates that the second chirp is delayed relative to the first. Except for P1 measured at BNL, all of the differences were positive, as expected. Yet again, high inter-subject variability was present in these measures. No statistically significant differences in latencies were found as a function of test condition. 28

Figure 4. Mean latency differences between first and second chirp responses plotted as function of listening condition. Latency prolongation is seen for all components except for P1-N1 in Quiet and at BNL. Error bars indicate standard deviation.

Amplitude ratios were calculated by dividing the peak-to-trough amplitude of the response to the second chirp by that of the component amplitude found in response to the first chirp. Figure 5 shows the mean amplitude ratios as a function of test condition. There were some exceptions to the expectation that the amplitude to the second chirp would be reduced relative to the first. This is seen for P1 in quiet and at BNL, and also for N1-P2 at BNL. There were no statistically significant differences in amplitude ratio as a function of test condition; however, there was a trend towards significance (F2, 16=2.712, p=0.0827) for the N1-P2 amplitude ratio.

This trend shows an increase in amplitude ratio from 0.66 in the quiet condition to 0.99 in the

BNL condition, indicating possible reduced suppression with the introduction of babble noise. 29

Figure 5. P1, N1, P2 mean amplitude ratios from first chirp to second chirp as a function of condition. Error bars indicate standard deviation.

Recall that these two chirp paradigm analyses only included data from individuals with all components present in within a given latency window. Table 1 summarizes the number of missing components in each condition. There were 12 missing components in response to the first chirp, out of a possible 153 (17 subjects x 3 components x 3 listening conditions). There were 19 missing components in response to the second chirp. There were 7 subjects who had all components present in the Quiet condition, 5 subjects who had all components present with

BNL, and 8 subjects who had all components present at the New BNL.

Cortical Auditory Evoked Potential Number of Missing Component Components First Chirp P1 Quiet 2

First Chirp P1 BNL 5 30

First Chirp P1 New BNL 2

First Chirp N1 Quiet 0

First Chirp N1 BNL 2

First Chirp N1 New BNL 0

First Chirp P2 Quiet 0

First Chirp P2 BNL 1

First Chirp P2 New BNL 0

Second Chirp P1 Quiet 4

Second Chirp P1 BNL 4

Second Chirp P1 New BNL 3

Second Chirp N1 Quiet 2

Second Chirp N1 BNL 2

Second Chirp N1 New BNL 1

Second Chirp P2 Quiet 1

Second Chirp P2 BNL 1

Second Chirp P2 New BNL 1

Table 1. Number of missing cortical auditory evoked potential components as a function of test condition.

Experiment 2 Figure 6 plots the mean MCL, BNL and ANL values obtained when signal and noise were presented bilaterally. The addition of a cognitive load decreased the BNL from 43.5 dB HL to 29 dB HL, and the difference in ANL was 14.5 dB HL (SD= 16.3). A paired comparison t-test indicated that this difference owing to cognitive load was statistically, significant, t10= -2.951, p

= 0.0145. 31

Figure 6. Results of behavioral tests obtained from traditional, bilateral presentation of speech and noise. The mean values for MCL, BNL, and ANL determined without a cognitive load (purple bars) and with the addition of a cognitive load (red bars), and the difference between the BNL with and without a cognitive load (blue bar). Error bars indicate standard deviation.

Comparison of Results from Lateralized vs. Binaural Test Conditions.

To test hypothesis 4, a regression analysis comparing the ANL scores determined with a cognitive load in the binaural condition are plotted as a function of ANL found in the spatially- separated condition (Figure 7). Although there were 11 participants, only 10 could be included in the comparison due to 1 extreme outlier. The correlation measured was R= 0.707, and r2= 0.5. 32

Figure 7. Regression results of unilaterally presented new ANL to bilaterally presented new ANL, obtained with a cognitive load.

Correlation of ANL and CAEP Data

A number of correlation analyses were undertaken including a) unilateral ANL with unilateral “new” ANL (cognitive load added), b) unilateral ANL and unilateral new ANL with amplitude ratios (AR) for P1, for N1 and for P2, c) bilateral ANL with bilateral new ANL, d) bilateral ANL and bilateral new ANL with amplitude ratios for P1, N1, and P2 e) unilateral ANL and unilateral new ANL with bilateral ANL and bilateral new ANL 33

e) unilateral and bilateral ANL and “new” ANL with P1, N1, P2 latencies for the first and second

chirp.

For all correlations, the a priori criterion for significance was r≥0.50. Table 2 summarizes the

results of these analyses, a-d. The correlations that met the r≥0.50 criterion are highlighted in the

table. With only two correlations to amplitude ratios out of 12 meeting the criteria, these findings

appear to be random.

Unilateral Unilateral Bilateral P1-N1 N1-P2 P2-N2 New ANL ANL New AR AR AR ANL Unilateral 1.000 .722 .417 .218 .473 .520 New ANL Unilateral .721 1.000 .361 .294 .218 .429 ANL Bilateral .417 .361 1.000 .508 .399 .317 New ANL Bilateral .475 .533 .149 -.034 .374 .204 ANL Table 2. Summary of correlations between behavioral data and cortical auditory evoked potential amplitude ratios. Highlighted correlations indicate a criterion of r≥0.50.

Table 3. Summary of correlations between behavioral data and cortical auditory evoked potential latencies. Highlighted correlations indicate a criterion of r≥0.50. Chirp 1 Chirp 1 Chirp 1 Chirp 2 Chirp 2 Chirp 2 Unilateral Bilateral Bilateral P1 N1 P2 P1 N1 P2 ANL ANL New Latency Latency Latency Latency Latency Latency ANL Unilateral -.510 -.094 -.249 -.253 -.148 .509 1.000 .339 .255 ANL Unilateral -.141 .082 -.167 -.500 -.146 .729 .721 .230 .075 New ANL Bilateral -.079 .089 -.051 -.577 .030 -.187 .339 1.000 -.263 ANL Bilateral .233 .075 -.583 -.087 -.758 -.101 .255 -.263 1.000 New ANL

Table 3 summarizes the results of the correlations performed for the latency data. The correlations that met the r≥0.50 criterion are highlighted in the table. A moderate correlation was found between Unilateral New ANL and P2 latency and Bilateral New ANL and N1 latency; however, no other correlations reached this significance.

Bilateral ANL participants were divided into two groups from their behavioral data.

Specifically, those with an ANL of <6 were group 1 (7 participants) and those with an ANL of

8+ were in the second group (4 participants). Overall, a regression analysis showed that there was no significant association between the cortical evoked potential latencies and the ANL.

DISCUSSION

The current study aimed to determine if a cognitive load affects a person’s acceptance of noise. A second aim was to determine the electrophysiological correlates of the ANL, with and without a cognitive load. We hypothesized that, 1) ANL would be affected by a cognitive load,

2) there would be a relationship between cognitive performance in noise and a CAEP test using a

1 chirp paradigm, and 3) there would be a relationship between cognitive performance in noise and a CAEP test using a 2 chirp-sensory gating paradigm.

The results of the current study found that there is a 22dB difference in how much noise can be tolerated during a dual working memory task when noise is presented unilaterally and a

14 dB difference when noise is presented bilaterally. This is a clear cognitive effect shown in normal hearing individuals. These findings support the hypothesis that ANL would be affected by a cognitive load. The bilateral noise results are in agreement with Pichora-Fuller, Schneider, and Daneman (1995) and Helps, Bamford, Sonuga-Barke, and Söderlund (2014), who found a decline in cognitive performance with the presence of noise. Ahlstrom, Horwitz, and Dubno 35

(2009) measured ANL with lateralized/spatially separated noise and compared it to a collocated

ANL condition. They found that ANL scores significantly improved with spatially separated noise, in agreement with our findings. Ahlstrom concluded that this may provide insight into directional benefit of hearing aids.

There are speculative reasons why there are greater differences in the tolerance of bilateral noise/speech compared to unilateral noise/speech. Specifically, the current study found that less noise was tolerated in the monaural condition compared to the binaural, especially with an added cognitive load. One possible cause of this difference is the binaural vs. monaural advantage. Nábelek and Robinson (1982) evaluated this advantage in a reverberant noise condition. Specifically, the researchers measured the speech perception performance with monaural and binaural presentations in the presence of reverberant noise in the sound field. They concluded a binaural advantage that ranged from 3.8-6.7% of age groups 10-72 years old. The averaged 5% binaural advantage is represented as binaural squelch, showing that listening with two ears can help reduce the distraction of noise to better improve speech perception (Nábelek &

Robinson, 1982). MacKeith and Coles (1971) went on to analyze speech perception and binaural squelch using continuous wide-band speech-shaped noise in the sound field and the phonetically balanced (PB) word lists in monaural and binaural conditions. The researchers varied the location of the loudspeaker but overall found a 3dB SNR binaural advantage which they also concluded was representative of binaural squelch. MacKeith and Coles concluded that squelch is a result of the summation of sound energy that helps improve perception of speech in noise when the listener is utilizing a binaural condition.

The results from the current study help support the notion that binaural squelch is adding to the difference in how much noise can be tolerated in the binaural vs. monaural conditions. 36

This finding may be useful to counsel patients on the importance of bilateral amplification/listening. Nobel (2005) reviewed the benefit of bilateral and unilateral hearing aid fittings on hearing speech, speech in noise, clarity, listening effort, and more. Nobel found a reoccurring theme that bilateral hearing aid use reduced listening effort, improved speech clarity, improved hearing in general, and provided some benefit to speech in noise. Additional studies have reviewed the benefit of bilateral and unilateral amplification of cochlear implants. Ching,

Incerti, and Hill (2004) evaluated the functional performance in 21 adults who used a cochlear implant in one ear and compared it to the benefits of adding a hearing aid to the contralateral ear.

Using the BKB/A sentences and 8-talker babble noise, the participants completed assessments for cochlear implant alone, cochlear implant plus hearing aid (bilateral), and hearing aid alone conditions. Ching et al., showed that the bilateral condition resulted in significantly better performance compared to both of the unilateral conditions in the lab and functional performance in real life. In regard to binaural squelch, Vermeire, and Van de Heyning (2009) found that a bilateral hearing aid plus cochlear implant condition resulted in a 3.8dB positive effect compared to the unilateral condition. These studies help to support the current study’s findings that when speech noise is presented binaurally, the tolerance is better compared to when speech and noise is spatially separated, and likely due to the binaural squelch effect.

The results conflict with a recent study showing that noise did not affect working memory performance (Marrone, Alt, DeDe, Olson, Shehorn, 2015); although, different methodologies were incorporated. For example, the authors in the Marrone et al. study used verbal working-memory tasks with both closed and open set tasks. Specifically, in the closed task, participants were asked to repeat numbers aloud after subtracting 2 from a set of randomized numbers. The open set task incorporated the Alphabet Span. Participants were 37 played a set of words and then asked to rearrange the words in alphabetical order. These tasks were completed in a quiet condition and in a noise condition with steady-state speech-spectrum- shaped noise. The present study incorporated a dual-task in multi-talk babble, differing from the

Marrone et al. research. Pichora-Fuller et al. (1995) suggested that the impact of noise on working memory is likely affected by the complexity of the cognitive demand, leading to a possible explanation for the contradicting findings. With increased complexity of cognitive demand and noise, more processing resources are needed which can negatively affect performance on behavioral tests (Pichora-Fuller et al., 1995).

Cognition is an important factor that needs to be considered for people seeking amplification. A short-coming of the ANL test is that it does not account for the effects of cognitive load. People with hearing loss, despite amplification, are likely to require more listening effort, especially in noise. Adding a cognitive load to the ANL test may be a better predictor of who will use amplification more consistently.

Electrophysiology Along with Brannstrom et al. and Griffitts and Cone (2015), we were unable to replicate the findings of Tampas and Harkrider (2006) who found that lower ANL scores resulted in smaller CAEP amplitudes. Tampas and Harkrider suggest that those with poor acceptance of noise (higher ANL scores) may have poorer neural synchrony as it relates to the increased wave

V latency. We were unable to demonstrate a systematic relationship between ANL and CAEP amplitudes. There were numerous correlations that exceeded an r of 0.50 for CAEP amplitude ratios and latencies and ANL. These results need further exploration to determine the meaning of the findings. 38

It has been shown that neural synchrony can be disrupted by the introduction of noise

(Samira et al., 2010; Burkard and Sims, 2002). However, we were unable to find any latency difference with respect to the noise conditions and thus no conclusion about neural synchrony can be determined. Additionally, Tampas and Harkrider suggested that those with better tolerance of noise have greater sensory gating. When tested empirically, as we did with the 2- chirp experiment, we did not find this relationship.

It is important to keep in mind that our cortical evoked potential components, especially in the 2-chirp sensory gating paradigm showed large variability in latency differences and amplitude ratios. As mentioned previously, research on the chirp at the level of the auditory cortex has not thoroughly been explored. Variability may be, in part, explained by the choice of our non-traditional stimuli compared to the current literature. Additionally, the range of ANL scores were not as great as in Tamps and Harkrider’s study. For 21 normally hearing subjects in

Tampas and Harkrider, they had groups of low ANL scores (less than 6) and high ANL scores

(more than 16) with no participants scoring in between. In the current study, our bilateral range of scores was -2-+14 and in Griffitts and Cone (2015), in a sample of 18 subjects, the range was only 1-16. It is puzzling that Tampas and Harkrider were able to find such a wide range of scores drawing from a population similar to ours (i.e., university students). Specifically, there was only 1 score that was considered a high ANL and 1 that was considered a low ANL whereas there were 10 considered high and 11 considered low in the previously referenced study.

Conclusions and Clinical Implications Acceptable noise levels were increased when a cognitive load was added to the test. The

ANL test is designed as a hearing aid fitting measure for determining the likelihood of someone being a full time, part time, or non-user of hearing aids. This pre-measure is predicted with an 39 accuracy of 85%; however, it does not take into consideration when a person has an increased cognitive load. The average American encounters stress every day in their life including work and home life, money problems, health inequality, discrimination and more (American

Psychological Association, 2016). Additionally, the APA reported the average stress level is 6/10 for millennials, 5.8/10 for Generation X, and 4.3/10 for Baby Boomers. Sandi (2014) reported that stressors can affect a person’s cognitive load negatively. With the change in ANL that we found with increasing the cognitive load, some participants switched from being in the full-time or part-time user group to the non-user group. If a participant tolerates less noise with a cognitive load is added and switches into another group, this information can be a useful counseling tool for hearing aid candidates. Realistic expectations are a key part of hearing aid success and consistent use. The audiologist can discuss with the patient when they are under stress or if dealing with a taxing problem, the hearing aids will reach a limit to how beneficial they can be.

The present study was able to determine a significant decrease on performance for a cognitively loaded task when noise was presented in a bilateral condition and when the noise was spatially separated from the signal. According to the World Health Organization, (2011), 40% of the European Union is exposed to traffic noise that exceed 55 dB(A). With noise being such a prominent factor in a person’s life, it is important to consider the impact it has on a person’s cognitive ability and stress level, especially when counseling a person on expectations with hearing loss.

Further research is needed to determine how much noise is needed to affect a person’s cognitive load and if the complexity of the task itself is relevant. The relationship between ANL and CAEPs is still yet to be determined. The conflicts in the current literature prompt for more comparisons of acceptance of noise and reduction in amplitudes. If a relationship can be 40 determined between acceptance of noise or cognitive load on a person’s CAEPs, this can be used as a more objective way of determining the likelihood of hearing aid use.

APPENDIX A – DATA COLLECTION INSTRUMENTS

Pre-Study Questionnaire Answering YES to any of the following statements does not necessarily disqualify you from participating in this study

For statements 1-6, please indicate YES if any are TRUE. You are not required to disclose which statement(s) you answered yes to.

1. English is not my primary language. 2. I have had a head injury causing loss of consciousness. 3. I have been diagnosed with a cognitive impairment by a speech pathologist, neuropsychologist, or medical doctor. 4. I have been diagnosed with a learning disability, auditory processing disorder, and/or ADHD. 5. I am currently taking medications/drugs that might make me drowsy, hyperactive, or disoriented (e.g. anti-psychotics, sedatives, barbiturates, anti-convulsants (seizure), Ritalin, etc.). 6. I have had ear surgery.

YES to one or more of the above questions NO to all of the above questions

Please check YES or NO to statements 7-11.

YES NO 7. I have a difficulty hearing and/or understanding speech in the presence of background noise. 8. I have a difficulty understanding degraded speech (e.g., rapid speech, muffled speech). 9. I have a difficulty following spoken instructions in the classroom in the absence of language comprehension deficits. 10. I have a difficulty discriminating and/or identifying speech sounds. 11. I inconsistently respond to auditory stimuli and/or cannot attend to auditory stimuli for more than a few seconds. 42

APPENDIX B – ADMINISTERING THE ACCEPTABLE NOISE LEVEL

TEST 43

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