The Developing Relationship Among Cognition, Amplification, and Aural Rehabilitation The search for best practices in hearing aid fittings and aural rehabilitation has generally used the audiogram and function stemming from peripheral sensitivity. In recent years, however, we have learned that individuals respond differently to various hearing aid and aural rehabilitation techniques based on cognitive abilities. In this paper, we ... Article
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Article  |   October 04, 2016
The Developing Relationship Among Cognition, Amplification, and Aural Rehabilitation
Author Affiliations & Notes
  • Jeffrey J. DiGiovanni
    Communication Sciences and Disorders, Ohio University, Athens, OH
  • Travis L. Riffle
    Communication Sciences and Disorders, Ohio University, Athens, OH
  • Disclosures
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  • Financial: The authors have no relevant financial interests to disclose.
    Financial: The authors have no relevant financial interests to disclose.×
  • Nonfinancial: The authors have no relevant nonfinancial interests to disclose.
    Nonfinancial: The authors have no relevant nonfinancial interests to disclose.×
Article Information
Hearing Aids, Cochlear Implants & Assistive Technology / Audiologic / Aural Rehabilitation / Attention, Memory & Executive Functions / Part 2
Article   |   October 04, 2016
The Developing Relationship Among Cognition, Amplification, and Aural Rehabilitation
Perspectives of the ASHA Special Interest Groups, October 2016, Vol. 1, 47-54. doi:10.1044/persp1.SIG6.47
History: Received June 1, 2016 , Revised June 30, 2016 , Accepted July 1, 2016
Perspectives of the ASHA Special Interest Groups, October 2016, Vol. 1, 47-54. doi:10.1044/persp1.SIG6.47
History: Received June 1, 2016; Revised June 30, 2016; Accepted July 1, 2016

The search for best practices in hearing aid fittings and aural rehabilitation has generally used the audiogram and function stemming from peripheral sensitivity. In recent years, however, we have learned that individuals respond differently to various hearing aid and aural rehabilitation techniques based on cognitive abilities. In this paper, we review basic concepts of working memory and the literature driving our knowledge in newer concepts of hearing aid fitting and aural rehabilitation.

Almost forty years ago, Plomp (1978)  performed an extensive analysis to explain how sensorineural hearing loss consists of two components: attenuation and distortion. Even at that time, the meaning of attenuation was readily clear: thresholds are raised (most likely) due to sensory mechanism deficits. While the distortion component was a widely observed occurrence, the attribution of it remained a mystery. As a result, Plomp argued, hearing aids may provide amplification to ameliorate attenuation loss but could not do much to help with the distortion.
Couched in similar critique of hearing care and hearing devices, Killion (1997)  offered an account of the distortion mystery by explaining vast variations in speech-in-noise performance for any given hearing loss. By reviewing published data, he demonstrated that we cannot predict reliably, for any given patient, his/her speech-in-noise performance based on his/her hearing loss alone. In Plomp's terms, a given attenuation (threshold shift) does not allow one to predict distortion. Killion suggested that variability in inner-hair versus outer-hair cell loss explains these differences and that the only solution is improving the signal-to-noise ratio (e.g., through improved hearing aid technologies; Killion, 1997). This short work in one of our popular trade journals demonstrated the progress in our understanding and yet was still limited to the auditory periphery. We now understand that in addition to high-integrity input signals, an individual's ability to maintain attention, hold items in short-term memory, process tasks efficiently, and his/her greater knowledge has dramatic impacts in auditory performance with and without hearing aids.
Our understanding of the hearing mechanisms that explain performance has become increasingly sophisticated in recent years and has evolved far beyond cochlear mechanics. We are beginning to develop a firm concept of the cognitive processes that are engaged in various listening situations that can not only explain differences in performance, but can guide hearing aid fitting and aural rehabilitation. As we grow to understand these processes, namely working memory and attention, ideally we will no longer fit to the audiogram exclusively, but rather combine traditional measures with patient performance and capabilities in these higher-level domains to program hearing aids. In addition, we will have the ability to customize and target performance areas in aural rehabilitation. We provide a broad overview of working memory as it relates to normal hearing. Following this overview, we discuss how hearing loss impacts this and how hearing aids can be leveraged to maximize benefit. Finally, we will discuss how research in this area is leading to far more sophisticated techniques in aural rehabilitation.
Working memory is more involved than simple short-term memory. From a functional point of view, we progress through our lives simultaneously remembering various items while performing some degree of processing. This dual-function of short-term memory and processing is what we refer to as working memory. A classic method of measuring a person's abilities in working memory is by employing the reading span task (Daneman & Carpenter, 1980). This test requires a person to read a series of sentences out loud and, at the end of the reading, recall the last word in each sentence. The greater the number of words that can be recalled, the higher the working memory capacity (WMC) is said to be.
The level of working memory involvement differs depending on the type (i.e., bottom-up versus top-down) of processing required. Bottom-up, or implicit, processing involves the integrity of the acoustic signal. If one is listening to a speaker in a quiet environment, the speech includes all the redundant cues with minimal extraneous sounds. As such, the signal itself is easily parsed and interpreted into a speech signal. However, if a listening environment consists of substantial noise (even if that noise does not impact intelligibility) the signal quality is sufficiently low and would require higher level processing (Rönnberg et al., 2013). This top-down, or explicit, processing helps fill in the missing pieces by using our general knowledge, vocabulary, and context. Naturally, top-down processing requires considerably more resources and thus is a more effortful experience.
A well-established practice in audiology prescribes the use of speech intelligibility measures (in quiet or noise) to assess listening performance with or without hearing aids. The relevance, however, of this measure is limited by an incorrect assumption. It was reasoned that for a given level of intelligibility, performance in real-world situations would be similar. Unfortunately, this proved less than ideal because language comprehension involves complex cognitive mechanisms that are not fully engaged by speech intelligibility measures (McCoy et al., 2005). Stated another way, speech intelligibility is largely driven by bottom-up processing, while comprehension involves both bottom-up and top-down processes (Jung, 2010). To test functional outcome measures such as language comprehension and listening effort clinically, the assessment would also have to involve other engaged mechanisms, namely working memory. With its high correlation to language comprehension, tests of working memory afford that ability as well as being far less involved to implement than language comprehension tests. Clinical tests of working memory have been developed; and while currently too long to be accepted clinically, they still only take a fraction of the time for a language comprehension task (Rönnberg, Stenfelt, & Rudner, 2011).
As our understanding of the short-term memory and processing components of working memory has become more sophisticated, the role of attention has become a focus of study. This is particularly important in listening situations with or without hearing aids as our world is laden with extraneous sounds competing for our attention as we work to maintain focus on our preferred signal. For those with hearing loss, we once thought that technology would solve these problems eventually. As we discuss later, we see that individual abilities may predict not only hearing aid performance, but inform the best programming strategies for that particular patient. As such, it is not simply a technology solution, but rather a match between available technologies and an individual patient's WMC.
In consideration of real-world listening situations, studies have been designed to ascertain one's ability to maintain focus on a task in the presence of distractors. It is intuitive that the more static a distracting sound is, the more easily one might become accustomed to the sound and there would be no substantial interference in one's listening. However, distracting sounds that change over time, or have occasional changes, substantially impact one's cognitive performance (Sörqvist, 2010). In the study Sörqvist (2010)  published, subjects were presented with a sequence of the same consonant (e.g., “c c c c c c c”), the same sequence except for one consonant (i.e., deviant; e.g., “c c c m c c c”), or completely different consonants (e.g., “k l m v r q c”) while performing a mathematical task presented visually. There was also a word shown after the mathematical task that was to be recalled later in the experiment. As such, the mathematical task imposed a processing requirement while the words imposed an increasing memory load as the experiment progressed. The spoken consonants served to assess how well attention might be maintained. He found that individuals with greater abilities in short-term memory and processing (i.e., a greater WMC) were resistant to the deviant presentations, suggesting that higher working memory predicts better ability to switch attention as well as an increased ability to perform secondary-memory retrieval.
Building on the theme that WMC might predict abilities in particular tasks, studies have been performed to increase our understanding. In a multi-modality study, Sörqvist, Nöstl, and Hanlin (2012)  tested the hypothesis that persons with higher WMC would acclimate to changes in distraction. To test this, they presented arrows pointing leftward or rightward on a computer monitor, for which the subjects were to indicate which direction by pressing the left or right arrow buttons on the keyboard. Simultaneously, a distracting tone was presented under headphones. About 10% of the time, the tone changed frequency for a single presentation, thus creating a deviant distractor. They found that subjects with higher WMCs began acclimating to the deviant distractors after the first set of presentations whereas the subjects with lower WMCs showed no signs of acclimatization even after six sets of presentations (Sörqvist et al., 2012).
Long-standing discussions have transpired as to whether working memory is modality-specific, and to what extent it is modality-specific. We will place the arguments into two camps, noting the risk of oversimplification. On one side, the argument is that working memory is completely generic to the input (i.e., visual, auditory, etc.) Therefore, findings in one modality would extend to others. A list of words stored from visual or equivalently presented auditory stimuli would have identical results. On the other hand, if there are mechanisms that are specific to the mode of input, then performance would be specific to the mode of presentation. Moreover, the interference effects of multi-modal presentations would create new challenges in interpreting the data.
It is generally agreed that there are several components of working memory that are central and therefore common to the various modes of input. However, it has recently been convincingly argued that there are storage mechanisms specific to the mode of input. As such, there is a “peripheral” store dedicated to sound input, and another to visual input; these do not interact (Cowan, Saults, & Blume, 2014). In order to truly understand the relationship of working memory and auditory performance, we must employ tasks that engage the elements within auditory working memory, thus requiring signals and distractors to be auditory.
To explore Sörqvist et al.'s (2012)  findings in a way that would enable application to auditory performance, we replicated and expanded his study. To do this, we developed three auditory tasks that included a memory load and processing load in the presence of distractors. Similar to Sörqvist et al. (2012), 10% of the distractors were deviant. We found that both high and low WMC groups improved significantly after the first presentation set, which was different from Sörqvist (DiGiovanni, Riffle, Gilmore, & Mikol, 2016). We extended to ascertain the extent to which subjects would be resilient to new deviant distractors once acclimating to the original distractors. We found that both groups were impacted to a less extent than the original task and were able to acclimate quickly, demonstrating some level of resilience (DiGiovanni et al., 2016). Overall, we showed that individuals with greater cognitive resources can not only adapt more quickly to distractors, but are also more resilient to new distractors than those with fewer cognitive resources.
The links between hearing loss, hearing aids, and cognition have not always been understood. Around the turn of the millennium, psychological factors such as motivation, personality, and expectations (Gatehouse, Naylor, & Elberling, 2003) began emerging as important points of consideration when fitting hearing aids. Implicating these factors during fittings has helped foster the idea that hearing aid candidacy and fittings were a multifaceted endeavor, not based solely on pure-tone averages (PTA). The bulk of data has been focused on finding the most effective compression setting when accounting for an individual's ability to store and process (i.e., working memory), but more recent studies have examined other types of complex processing as well.
We have known for some time that while digital noise reduction (DNR) algorithms may improve the overall signal-to-noise ratio (SNR), it does not increase the SNR in any given processing band. The consequence of this is that speech intelligibility does not improve with this technology. Yet, studies have shown with striking consistency that patients prefer DNR (Dillon & Lovegrove, 1993; Keidser, 1996). It turns out that we were asking the wrong question. Using speech intelligibility as a primary outcome measure is attractive since it is eminently quantifiable and has the intuitive assumption of knowing that what cannot be received cannot be understood. But that assumption turns out to have greater limitations than once thought. The more germane question targets overall functional performance in difficult listening situations that have relevance to everyday life. These are difficult studies to design being fraught with variables that are troublesome to control, yet robust and replicable findings are being made. In one study, a carefully designed task that included speech-in-noise with and without DNR showed that while the DNR did not impact intelligibility, it appeared to reduce the cognitive load allowing subjects to perform the task more quickly (Sarampalis, Kalluri, Edwards, & Hafter, 2009). This was one of the first demonstrations that offered a reasonable and cogent explanation as to why patients systematically prefer DNR, despite it having no measurable intelligibility benefit.
Gatehouse, Naylor, and Elberling (2003)  were some of the early frontrunners of explicitly drawing the connection between hearing aid benefit and cognitive measures. Their study was one of the first to use a cognitive ability score as a predictor for speech identification using various hearing aid fitting programs (linear vs. nonlinear, slow vs. fast attack and release times) and background noise conditions (steady vs. modulated noise). Gatehouse et al. (2003)  discovered that while audibility remained a key factor for intelligibility, there were also interactions between the background noise condition, hearing aid fitting program, and cognitive ability. They observed that subjects performed better when the noise included temporal modulation—the modulated noise was a speech-spectrum noise that had the temporal envelope of a 2- or 6-person multi-talker babble. The modulation caused temporal dips (momentary periods of low-energy noise) and thus provided opportunities for elements of the speech signal to be more easily parsed from the noise. This phenomenon, known as “dip-listening,” results in more intelligible speech than listening in a steady-state noise. Results of the experiment showed that fast-acting compression settings gave better performance in modulated noise and those with higher cognitive capacity demonstrated improved dip-listening abilities, suggesting that those with higher cognitive capacity can perform better with fast-acting compression.
Gatehouse and colleagues (2006)  sought to better understand the relationship between cognitive capacity and proper choices of hearing aid signal processing strategies. They found that slow-acting automatic volume control (AVC) settings were more beneficial to those with lower cognitive capacity, and fast-acting wide dynamic range compression (WDRC) settings were more beneficial to those with higher cognitive capacity. Slow-acting AVC is designed to maintain the integrity of the input signal to match the dynamic range of the hearing aid user, while fast-acting WDRC is a more aggressive form of signal processing that sacrifices a bit of signal integrity for the tradeoff of increased short-term audibility. Gatehouse et al. (2006)  argued that those with lower cognitive capacity benefit from the improved signal quality of slow-acting AVC, but struggle with fast-acting WDRC because the aggressive signal processing distorts the signal and they are unable to fill in missing information as efficiently. On the other hand, those with high cognitive capacity are able to benefit from fast-acting WDRC (albeit at the expense of a slightly deteriorated signal) by utilizing top-down processing to make up for the missing auditory information. However, it was later found that until users have had the ability to acclimate to the new settings, the benefits of this WMC-specific programming may not be realized (Foo, Rudner, Rönnberg, & Lunner, 2007).
Souza and Sirow (2014)  executed a carefully-designed study to determine preferred hearing aid compression settings as informed by an individual's WMC. Their goal was to provide more clinically-relevant recommendations noting that it is often difficult to adjust certain parameters directly (e.g., attack/release times). They used a reading span task as a measure of WMC and aided speech-in-noise thresholds as the outcome measure (Killion, Niquette, Gudmundsen, Revit, & Banerjee, 2004). The experimenters tested three different hearing aid manufacturers with a total of four compression parameters: two slow (≥1000 ms) and two fast (≤75 ms). Aid B had a slow and fast setting allowing for within-aid comparison. A regression analysis was performed to determine the amount of variance contributed by WMC, PTA, and age. The results showed that for the slow compression release settings, performance was similar between low and high WMC groups. However, using fast compression release settings, the high WMC group performed significantly better than the low WMC group. Furthermore, as compression release settings became faster, the low WMC group's performance steadily deteriorated while the high WMC group's performance continued to improve. The final model of the regression analysis for the slowest compression setting showed that hearing loss and age explained significant amounts of variance, but WMC did not contribute. On the other hand, in the fastest compression setting, hearing loss and WMC contributed significant amounts of explained variance but age did not play a role. Souza and Sirow's results demonstrated that when using fast compression release settings, WMC is a major contributing factor to speech recognition performance, but does not play an important role in performance for slow compression release settings. This supports earlier findings arguing that WMC plays an important role when selecting hearing aid parameters and extends those findings from highly-controlled experimental environments to more realistic clinical settings.
The consistent finding that more complex compression settings are not only more beneficial to those with high cognitive capacities, but can actually be deleterious for individuals with low cognitive capacities, extends to signal processing domains other than amplitude compression as well. Arehart, Souza, Baca, and Kates (2013)  sought to measure the relationship between frequency compression type of transposition and working memory as measured by the reading span task. In their experiment, Arehart et al. (2013)  measured sentence intelligibility using various frequency compression settings in babble noise. Their regression model showed that working memory accounted for over 29% of the variance. They also found that as the frequency compression became more aggressive and, thus, the signal more distorted, subjects with higher working memory performed better than those with low working memory.
Taken together, these studies tell the same story. Simple processing, one that maintains the integrity of the input signal, is most beneficial for those with lower WMCs. Distortion is an inevitable by-product of signal processing. Amplitude compression distorts the signal, and as the compression becomes increasingly more aggressive (i.e., faster onsets/offsets and higher compression ratios), the amount of distortion being introduced increases as well. Historically, we thought that the benefits outweighed drawbacks. Now we are learning that the benefits are limited to those with higher WMCs, and thus these more aggressive forms of processing should be considered for this population. The less distortion that is introduced via signal processing, the less a person needs to rely on explicit, or top-down, processing. It is precisely the top-down processing that someone with lower WMC would find less beneficial and as such, will need the original signal unaltered. On the other hand, individuals with high WMC can engage in top-down processing much more effectively. As such, they are more tolerant to signal distortions and can therefore repair the distortions more effectively as well as benefit from the processing scheme itself. In short, we now know for whom our original assumption holds allowing more effective processing choices.
The inclusion of a working memory task in the hearing aid fitting process provides a unique opportunity for clinicians to engage in discussions with patients about the complexities of communication and the role that cognition plays in listening. Hearing aid patients may be curious as to how reading nonsense sentences and remembering certain words relates to performance with their hearing aids. From the patient's perspective, hearing loss is often thought of as an issue isolated to the ear, absent any involvement from higher-level mechanisms. As noted earlier, the knowledge of an overt relationship between hearing loss and cognitive processing is a more recent development. But, as research is continuing to show, cognitive function is certainly an important factor to consider during hearing aid fittings. Being able to discuss why this is the case with patients may provide inroads to a more comprehensive and engaging counseling, and even rehabilitative, experience.
Given the advancements in hearing science research, aural rehabilitation (AR) has progressed commensurately in terms of illuminating the relationship between hearing loss and cognition. There are many different aspects of AR including identification and diagnosis of hearing loss, selection and fitting of amplification, acclimatization to new hardware, additional assistive listening devices (ALDs), counseling, speech and language therapy, auditory training, etc. All of these individual aspects of AR contribute to the general overall goal of AR: minimizing the impact that hearing loss has on a person's daily life. While the hardware (e.g., hearing aids, ALDs, etc.) is designed to transmit the highest quality signal possible, it is up to the listener to make use of this information once it is perceived. Just as cognition is becoming a factor in hearing aid amplification schemes, AR programs are beginning to utilize cognitive involvement in rehabilitation. Since everyday auditory scenarios require a balance between the moments of top-down and bottom-up processing, auditory training is optimal when it utilizes strategies from both of these processes to get the hearing aid user accustomed to real-world listening situations.
Several approaches to AR designed with cognitive aspects have been studied. Examples include semantic priming, stream segregation, spatial expectation, and emotional consistency. Staying true to the AR model, these areas incorporate both bottom-up and top-down elements. Goy, Pelletier, Coletta, and Pichora-Fuller (2013)  highlights the essence of the bottom-up/top-down duality approach of AR: younger and older adults were required to make lexical decisions about stimuli that varied in semantic context and the amount of acoustic distortion. Results showed that in general, both context and signal quality affected lexical decision reaction times; the benefit of having contextual information decreased as signal distortion increased, especially for older adults. Ameliorating the effects of poor signal quality could be addressed by particular amplification strategies and utilizing contextual information could be affected by AR training.
There are several auditory training programs available that include training in areas such as speechreading, time-compressed speech, listening in noise, competing talkers, auditory working memory, sound discrimination, loudness scaling, selective attention, and many others. As evidenced by the abundance of different “trainable” auditory skills, auditory training programs aim to improve all aspects of listening, ranging from perceptual discrimination to cognitive processes; again following the bottom-up/top-down approach. The concept of auditory training is that a person can use software to guide practice and become more efficient in different listening skills in a controlled environment. The goal of this process is that the user will develop better communication skills in a real-world environment. A systematic review by Sweetow and Palmer (2005)  studied the efficacy of individual auditory training in adults and found that it was difficult to find substantial data supporting its benefits. However, there was much variability between the studies in the review (e.g., differences in the study design, amount of training time, use of a control group, and so on). But given the inconsistencies in the studies, some trends were observable; namely that auditory training seemed to help hearing-impaired listeners engage more in active listening techniques, and that synthetic (top-down) training showed some improvements for speech recognition in noise, but analytic (bottom-up) training did not show any definitive results.
Broadly, some benefitted from auditory training while others did not, and some benefitted from placebo training or just general hearing aid counseling. While negative findings are not the most exciting ones, it seems that future AR/auditory training research should be individually prescribed for those who will benefit from auditory training. It's not a major leap to suggest that cognition is now a consideration and, similar to hearing aid processing research, AR techniques will be individually tailored to a person's WMC instead of traditional individual differences. Working memory measures have long been used to elucidate individual differences in performance, especially as the difficulty of a task increases. Recent research in our own lab has shown that those with higher WMC are less vulnerable to distracting noises, even when the noise changes or the task changes (DiGiovanni et al., 2016). Would a person with high WMC benefit from a training program that centered on ignoring certain sounds and focusing on others? It may be the case that this particular skill might already be near a ceiling limit for a person with high WMC and s/he might not benefit from the training. This could then suggest that those with low WMC may be the ideal candidates for auditory training since they have more room for improvement in this particular skill. But, then again, perhaps a difficult training program might overwhelm the available capacity in an individual with low WMC, especially in light of an existing hearing loss, and render the training ineffective. These are all important questions to bear in mind when considering the research and clinical employment of novel AR techniques.
In summary, recent research has shown that cognition plays a significant role in the success of amplification and aural rehabilitation. Some hearing aid users benefit from aggressive signal processing schemes while others are hindered by them. In the past, it may have been considered simply a preference of one scheme over another, but we now know with a degree of certainty that this results from differences in cognition: those with high WMC are able to minimize the impact of distortion by utilizing top-down information more efficiently than those with low WMC. Given that certain signal processing schemes can be deleterious for those with low WMC, developing a practical working memory task for clinical use as well as utilizing speech comprehension measures that invoke higher level top-down processing would be highly advantageous to hearing aid fittings. AR may be on a similar trajectory; as the research base grows, audiologists will be able to match AR techniques to the specific needs of a patient. We believe that future research in AR should include measures of WMC and its relationship to any benefits from AR training. In this manner, more prescriptive, individualized AR plans may be developed.
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Sarampalis, A., Kalluri, S., Edwards, B., & Hafter, E. (2009). Objective measures of listening effort: Effects of background noise and noise reduction. Journal of Speech, Language, and Hearing Research, 52, 1230–1240. [Article] ×
Sörqvist, P. (2010). High working memory capacity attenuates the deviation effect but not the changing-state effect: Further support for the duplex-mechanism account of auditory distraction. Memory & Cognition, 38(5), 651–658. [Article] [PubMed]
Sörqvist, P. (2010). High working memory capacity attenuates the deviation effect but not the changing-state effect: Further support for the duplex-mechanism account of auditory distraction. Memory & Cognition, 38(5), 651–658. [Article] [PubMed]×
Sörqvist, P., Nöstl, A., & Hanlin, N. (2012). Working memory capacity modulates habitiuation rate: Evidence from a cross-modal auditory distraction paradigm. Psychonomic Bulletin Review, 19, 245–250. [Article] [PubMed]
Sörqvist, P., Nöstl, A., & Hanlin, N. (2012). Working memory capacity modulates habitiuation rate: Evidence from a cross-modal auditory distraction paradigm. Psychonomic Bulletin Review, 19, 245–250. [Article] [PubMed]×
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Souza, P., & Sirow, L. (2014). Relating working memory to compression parameters in clinically fit hearing aids. American Journal of Audiology, 23, 394–401. [Article] [PubMed]×
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Sweetow, R., & Palmer, C. V. (2005). Efficacy of individual auditory training in adults: A systematic review. Journal of the American Academy of Audiology, 16, 494–504. [Article] [PubMed]×
We've Changed Our Publication Model...
The 19 individual SIG Perspectives publications have been relaunched as the new, all-in-one Perspectives of the ASHA Special Interest Groups.