Starkey Research & Clinical Blog

Listening gets more effortful in your forties

DeGeest, S., Keppler, H. & Corthals, P. (2015) The effect of age on listening effort. Journal of Speech, Language and Hearing Research 58(5), 1592-1600.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

The ability to understand conversational speech in everyday situations is affected by many obstacles. A large proportion of our work involves determining the best treatment plan to help hearing-impaired patients overcome these obstacles.  Though understanding speech in noise poses difficulty for hearing-impaired individuals of all ages, several studies have indicated that in the absence of hearing loss, older adults face increased challenges in noisy environments (Pichora-Fuller & Singh, 2006; Duquesnoy, 1983; Dubno et al., 1984; Helfer & Freyman, 2008); some reports suggest that middle-aged adults have significantly poorer speech recognition in noise compared to young adults. (Helfer & Vargo, 2009).

Competing environmental noise reduces the audibility of acoustic speech information, increasing reliance upon visual, situational and contextual cues, that in turn requires a greater delegation of cognitive resources (Schneider et al., 2002), making listening more effortful. Increases in listening effort in noise could be related to decreases in hearing thresholds or available cognitive resources, as both are known to decrease with advancing age.  But the fact that normal-hearing individuals also experience more difficulty hearing in noise suggests that factors other than hearing loss may be involved, including working memory, processing speed and selective attention (Akeroyd, 2008; Pichora-Fuller et al., 1995).

The work of DeGeest and colleagues examined listening effort and speech recognition in adult subjects from 20 to 77 years of age. All of the subjects were determined to have normal “age corrected” hearing thresholds from 250Hz through 8000Hz, though older subjects had average high-frequency pure tone thresholds in the mild to moderate range of hearing loss. Subjects over age 60 were screened with the Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005), no specific cognitive performance measures were included in data analysis.  Listening effort was evaluated using a dual-task paradigm in which subjects performed a speech recognition task while simultaneously performing a visual memory task. Speech recognition ability was measured with 10-item sets of two-syllable digits, presented at two SNR levels: +2dB SNR and -10dB SNR.  Performance on the dual-task presentation was examined in comparison to baseline measures of each test in isolation. Listening effort was defined as the change in performance on the visual memory task when the dual-task condition was compared to baseline. Speech recognition ability was not expected to change from baseline when measured in the dual-task condition.

The investigators found that listening effort increased in parallel with advancing age. Though subjects were initially determined to have “age corrected” normal hearing, which meant some participants had high frequency hearing loss, the correlation between listening effort and age was maintained even when the factors of pure tone threshold and baseline word recognition performance were controlled. Of note was the observation that listening effort started to increase notably between +2dB and -10dB SNRs at ages of 40.5 years and 44.1 years, respectively. Their determination that listening effort begins to increase in the mid 40’s is in agreement with other research that reported cognitive declines beginning around age 45 years (Singh-Manoux et al., 2012).  The authors suggest that further investigations of listening effort and word recognition in middle-aged and older adults should examine cognitive ability in more detail with specific tests of working memory, processing speed and selection attention included in the data analyses.

Although middle-aged adults are less likely to demonstrate outward effects of cognitive decline than older adults, the should not be regarded as immune to changes in cognitive ability and resulting listening effort.  Middle-aged individuals are more likely than their older counterparts to be working full time and may have more active lifestyles.  Hearing-impaired individuals of middle-age who work in reverberant or noisy environments may face additional challenges to job performance if they are also experiencing changes in processing speed or memory or if they struggle with even mild attentional deficits.  These are tangible considerations that might impact the entirety of treatment plan development, from the selection of hearing aids and assistive technologies to the communication and counseling strategies that are selected for the patient and their family members.

References

Akeroyd, M. (2008). Are individual differences in speech reception related to individual differences in cognitive ability? A survey of twenty experimental studies with normal and hearing-impaired adults. International Journal of Audiology 47 (Suppl 2), S53-S71.

DeGeest, S., Keppler, H. & Corthals, P. (2015) The effect of age on listening effort. Journal of Speech, Language and Hearing Research 58(5), 1592-1600.

Desjardins, J. & Doherty, K. (2014). The effect of hearing aid noise reduction on listening effort in hearing-impaired adults. Ear and Hearing 35 (6), 600-610.

Dubno, J., Dirks, D. & Morgan, D. (1984). Effects of age and mild hearing loss on speech recognition in noise. Journal of the Acoustical Society of America 76, 87-96.

Duquesnoy, J. (1983). The intelligibility of sentences in quiet and noise in aged listeners. Journal of the Acoustical Society of America 74, 1136-1144.

Helfer, K. & Freyman, R. (2008).  Aging and speech on speech masking. Ear and Hearing 29, 87-98.

Keppler, H., Dhooge, I., Corthals, P., Maes, L., D’haenens, W., Bockstael, A. & Vinck, B. (2010). The effects of aging on evoked otoacoustic emissions and efferent suppression of transient evoked otoacoustic emissions. Clinical Neurophysiology 121, 359-365.

Nasreddine, Z., Phillips, M., Bedirian, V., Charbonneau, S., Whitehead, V., Collin, I. & Chertkow, H. (2005).  The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society 53, 695-699.

Pichora-Fuller, M., Schneider, B. & Daneman, M. (1995).  How young and old adults listen to and remember speech in noise. The Journal of the Acoustical Society of America 97, 593-608.

Pichora-Fuller, M. & Singh, G. (2006). Effects of age on auditory and cognitive processing: implications for hearing aid fitting and audiologic rehabilitation. Trends in Amplification 10, 29-59.

Sarampalis, A., Kalluri, S. & Edwards, B. (2009). Objective measures of listening effort: Effects of background noise and noise reduction. Journal of Speech, Language and Hearing Research 52, 1230-1240.

Schneider, B., Daneman, M. & Pichora-Fuller, M. (2002). Listening in aging adults: from discourse comprehension to psychoacoustics. Canadian Journal of Experimental Psychology 56, 139-152.

Does hearing aid use slow cognitive decline?

Deal, J., Sharrett, A., Albert, M., Coresh, J., Mosley, T., Knopman, D., Wruck, L. & Lin, F. (2015). Hearing impairment and cognitive decline: A pilot study conducted within the Atherosclerosis Risk in Communities Neurocognitive Study. American Journal of Epidemiology 181 (9), 680-690.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

Recent evidence has suggested that cognitive decline and hearing impairment may have more of a connection beyond simple co-occurrence in the older population. Certainly, as individuals age, they become more likely to exhibit reduced cognitive function and also more likely to have hearing loss. It has been proposed that hearing loss may be correlated with temporal lobe and whole brain atrophy (Lin & Albert, 2014; Peelle, et al., 2011; Lin et al., 2014).  Whether the two conditions are related to a shared underlying cause is not known, but a number of studies have indicated that hearing loss may put older individuals at higher risk of cognitive decline (Lin, 2011; Lin et al., 2011; Lin, et al., 2013). The effect of hearing loss on cognition may be mediated by social isolation and loneliness or increased listening effort required to process speech via an impaired peripheral auditory system (McCoy, et al., 2005; Tun, et al., 2009). Conversely, cognition affects every-day communication and recent research has shown that hearing aid users with reduced cognitive capacity may have poorer speech recognition ability in noise, be more susceptible to the effects of distortion and noise and may also take a longer time to adapt to new hearing aids (Lunner, 2003; Lunner et al., 2009; Ng et al., 2014)

The work of Deal and colleagues aimed to determine whether older individuals with hearing loss show poorer cognitive performance and experience a more rapid rate of cognitive decline than those with normal hearing. Subjects were recruited from a population originally recruited in 1987-1989 for a longitudinal study called Atherosclerosis Risk in Communities (ARIC). Of the 15,792 ARIC subjects, 253 participated in this study on cognition and hearing, with a mean age of 76.9 years. Approximately 39% of the subjects were men, 61% were women.  At the 2013 session, 48% of the total participants reported ever smoking, 34% had diabetes and 71.9% had hypertension.  About 60% of the subjects had fewer than 12 years of education and 40% had more than 12 years of education.

The ARIC subjects completed a battery of neuropsychological tests on in three domains – memory, language and processing speed/attention – in 1990-1992 and again in 1996-1998.  Memory was tested with the Delayed Word Recall Test (DWRT; Knopman et al., 1989), the Incidental Learning Test (Kaplan et al., 1991) and the Logical Memory Tests I and II (Wechsler, 1945). Language was examined using the Word Fluency Test (Benton et al., 1994), Animals Naming Test (Goodglass & Kaplan, 1983) and the Boston Naming Test (Saxton et al., 2000). Processing speed and attention were assessed with the Digit Symbol Substitution and Digit Span Backwards Tests (Wechsler, 1981) and Trail Making Tests I and II (Spreen & Strauss, 1991; Reitan, 1958). For the purpose of the present study, these neuropsychological tests were administered again in 2013.

Pure tone air conduction thresholds were obtained for all 2013participants and they were categorized according to degree of loss indicated by the pure tone average (PTA) in the better ear: normal (lower than 25dB), mild (26-40dB), moderate/severe (greater than 40dB).  Only 5 individuals had PTAs greater than 70dB, so these individuals were included in the moderate/severe group. Of the total population, 34% had moderate/severe hearing loss, 37% had mild hearing loss and 29% had normal hearing. Hearing aid users made up approximately 20% of the total subject population. Hearing aid use was loosely defined as the self-reported use of a hearing aid in either or both ears during the month prior to the experimental session.  The duration of hearing aid use ranged from less than 1 year to 48 years, with most aided participants reporting hearing aid use for a period of 3 to 7 years.

All of the groups showed a decline in cognitive performance over the 20 years of the study, but the hearing loss groups declined faster than the normal hearing group. The subjects with moderate/severe hearing loss were slightly older and slightly more likely to be male and to have hypertension. However, after correcting for these variables, the subjects with moderate/severe hearing loss still declined significantly faster than the normal hearing group.

Approximately 51% of the subjects with moderate/severe hearing loss wore hearing aids.  The individuals who did not wear hearing aids had significantly poorer performance on the cognitive tests and demonstrated a significantly faster rate of decline compared to those in the moderate/severe group who did wear hearing aids. The rate of 20-year memory decline for the unaided individuals in this group was twice the average rate of decline reported in national studies of cognitive change in older adults (Salthouse, 2010; Hayden et al., 2011).  In comparison, the hearing aid users in this study with moderate/severe hearing loss showed a rate of cognitive decline that was only slightly higher than the rate for subjects with normal hearing.

The authors point out that because hearing was not assessed at earlier experimental sessions, they cannot rule out the possibility that cognitive decline had a causative effect on the measured hearing loss. However, this is unlikely because they corrected for co-occurring diseases and conditions in their analysis. Furthermore, conditions affecting cognition are not known to have any effect on the peripheral auditory system and cognitive deficits were not expected to have influenced the validity of the audiometric test results.

Many have proposed that hearing loss may increase risk of cognitive decline, via increased social isolation, increased perceptual effort and changes in brain volume. Unaided hearing loss is known to increase the risk of social isolation, which in turn has been associated with increases in blood pressure and corticosteroid levels, which could in turn affect brain structure (Mick et al., 2014; Hawkley & Cacioppo, 2010). Similarly, several studies have indicated that hearing loss increased effortful listening, thereby increasing the cognitive demands required to process speech (Rabbitt, 1968; Tun et al., 2009; McCoy et al., 2005).

The outcomes of this study are in agreement with other reports in which hearing impaired individuals demonstrated poorer performance on cognitive tests and faster rates of cognitive decline (Lin, 2011; Lin et al., 2011; Lin, et al., 2013). Other reports also indicate a relationship between hearing loss and subsequent dementia over years of follow-up evaluations (Gallacher et al., 2012; Lin et al., 2011).  The current outcome that hearing aid use had a mitigating effect on cognitive performance and rate of decline is fascinating and supports the need for further investigation on the relationship between cognition and hearing loss.

Though this is an emerging area of study, the results reported here offer strong support for the proposal that the risk of cognitive decline by hearing loss may be reduced, at least partially, by the correction of peripheral hearing loss with hearing aids.  This underscores the importance of amplification for older individuals and clinicians should be prepared to counsel their patients that hearing aids are an effective way to improve communication, decrease social isolation and may slow or decrease the risk of cognitive decline. However, clinicians should be cautious not to suggest that hearing aids will prevent cognitive decline. Although the authors are careful not to claim a causal relationship between hearing loss and cognitive decline, it is clear that the two conditions are related and because hearing loss is easily treatable it may be one of the few ways in which individuals can proactively manage their risk of cognitive decline.

References

Benton, A., Hamsher, K., & Sivan, A. (1994). Multilingual Aphasia Examination 3rd ed. Iowa City, IA: AJA Associates.

Deal, J., Sharrett, A., Albert, M., Coresh, J., Mosley, T., Knopman, D., Wruck, L. & Lin, F. (2015). Hearing impairment and cognitive decline: A pilot study conducted within the Atherosclerosis Risk in Communities Neurocognitive Study. American Journal of Epidemiology 181 (9), 680-690.

Gallacher, J., Ilubaera, V. & Ben-Shlomo, Y. (2012). Auditory threshold, phonologic demand and incident dementia. Neurology 79(15), 1583-1590.

Goodglass, H. & Kaplan, E. (1983). The Assessment of Aphasia and Related Disorders 2nd ed. Philadelphia, PA: Lea and Febiger: 102, 31.

Hawkley, L. & Cacioppo, J. (2010).  Loneliness matters: a theoretical and empirical review of consequences and mechanisms. Annals of Behavioral Medicine 40(2), 218-227.

Hayden, K., Reed, B. & Manly, M. (2011). Cognitive decline in the elderly: an analysis of population heterogeneity. Age and Aging 40(6), 684-689.

Kaplan, E., Fein, D. & Morris, R. (1991). WAIS as a Neuropsychological Instrument. San Antonio, TX: The Psychological Corporation.

Knopman, D. & Ryberg, S. (1989). A verbal memory test with high predictive accuracy for dementia of the Alzheimer type. Archives of Neurology 46(2), 141-145.

Lin, F.  (2011). Hearing loss and cognition among older adults in the United States. The Journals of Gerontology A: Biological Sciences and Medical Sciences 66 (10), 1131-1136.

Lin, F. & Albert, M. (2014). Hearing loss and dementia – who is listening? Aging and Mental Health 18(6), 671-673.

Lin, F., Ferrucci, L. & Metter, E. (2011). Hearing loss and cognition in the Baltimore Longitudinal Study of Aging. Neuropsychology 25(6), 763-770.

Lin, F., Yaffe, K., & Xia, J. (2013). Hearing loss and cognitive decline in older adults. Journal of the American Medical Association Internal Medicine 173 (4), 293-299.

Lunner, T. (2003). Cognitive function in relation to hearing aid use. International Journal of Audiology 42, (Suppl. 1), S49-S58.

Lunner, T., Rudner, M. & Ronnberg, J. (2009). Cognition and hearing aids. Scandinavian Journal of Psychology 50, 395-403.

McCoy, S.L., Tun, P.A. & Cox, L.C. (2005). Hearing loss and perceptual effort: downstream effects on older adults’ memory for speech. Quarterly Journal of Experimental Psychology A, 58, 22-33.

Mick, P., Kawachi, I. & Lin, F. (2014). The association between hearing loss and social isolation in older adults. Otolaryngology Head Neck Surgery 150(3), 378-384.

Ng, E.H.N., Classon, E., Larsby, B., Arlinger, S., Lunner, T., Rudner, M., Ronnberg, J. (2014). Dynamic relation between working memory capacity and speech recognition in noise during the first six months of hearing aid use. Trends in Hearing 18, 1-10.

Peelle, J., Troiani, V. & Grossman, M. (2011). Hearing loss in older adults affects neural systems supporting speech comprehension. Journal of Neuroscience 31(35), 12638-12643.

Rabbitt, P. (1968). Channel-capacity, intelligibility and immediate memory. Quarterly Journal of Experimental Psychology 20(3), 241-248.

Reitan, R. (1958). Validity of the Trail Making Test as an indicator of organic brain damage. Perceptual and Motor Skills 8, 271-276.

Salthouse, T. (2010). Major Issues in Cognitive Aging. Vol. 49, New York, NY: Oxford University Press: 246.

Saxton, J., Rafcliff, G. & Munro, C. (2000).  Normative data on the Boston Naming Test and two equivalent 30-item short forms. Clinical Neuropsychology 14(4), 526-534.

Spreen, O. & Strauss, E. (1991). A Compendium of Neuropsychological Tests: Administration, Norms and Commentary. 2nd ed. New York, NY: Oxford University Press.

Tun, P., McCoy, S. & Wingfield, A. (2009). Aging, hearing acuity and the attentional costs of effortful listening. Psychology and Aging 24(3), 761-766.

Wechsler, D. (1945). A standardized memory scale for clinical use. Journal of Psychology 19(1), 87-95.

Wechsler, D. (1981). Wechsler Adult Intelligence Scale – Revised. New York, NY: The Psychological Corporation.

Patients with higher cognitive function may benefit more from hearing aid features

Ng, E.H.N., Rudner, M., Lunner, T., Pedersen, M.S., & Ronnberg, J. (2013). Effects of noise and working memory capacity on memory processing of speech for hearing-aid users. International Journal of Audiology, Early Online, 1-9.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

Research reports as well as clinical observations indicate that competing noise increases the cognitive demands of listening, an effect that is especially impactful for individuals with hearing loss (McCoy et al., 2005; Picou et al., 2013; Rudner et al., 2011).  Listening effort is a cognitive dimension of listening that is thought to represent the allocation of cognitive resources needed for speech recognition (Hick & Tharpe, 2002). Working memory, is a further dimension of cognition that involves the simultaneous processing and storage of information; its effect on speech processing may vary depending on the listening conditions (Rudner et al., 2011).

The concept of effortful listening can be characterized with the Ease of Language Understanding (ELU) model (Ronnberg, 2003; Ronnberg et al., 2008). In quiet conditions when the speech is audible and clear, the speech input is intact and is automatically and easily matched to stored representations in the lexicon. When speech inputs are weak, distorted or obscured by noise, mismatches may occur and speech inputs may need to be compared to multiple stored representations to arrive at the most likely match. In these conditions, allocation of additional cognitive resources, is required. Efficient cognitive functioning and large working memory capacity allows more rapid and successful matches between speech inputs and stored representations. Several studies have indicated a relationship between cognitive ability and speech perception: Humes (2007) found that cognitive function was the best predictor of speech understanding in noise and Lunner (2003) reported that participants with better working memory capacity and verbal processing speed had better speech perception performance.

Following the ELU model, hearing aids may allow listeners to match inputs and stored representations more successfully, with less explicit processing. Noise reduction, as implemented in hearing aids, has been proposed as a technology that may ease effortful listening. In contrast, however, it has been suggested that hearing aid signal processing may introduce unwanted artifacts or alter the speech inputs so that more explicit processing is required to match them to stored images (Lunner et al., 2009). If this is the case, hearing aid users with good working memory may function better with amplification because their expanded working memory capacity allows more resources to be applied to the task of matching speech inputs to long-term memory stores.

Elaine Ng and her colleagues investigated the effect of noise and noise reduction on word recall and identification and examined whether individuals were affected by these variables differently based on their working memory capacity. The authors had several hypotheses:

1. Noise would adversely affect memory, with poorer memory performance for speech in noise than in quiet.

2. Memory performance in noise would be at least partially restored by the use of noise reduction.

3. The effect of noise reduction on memory would be greater for items in late list positions because participants were older and therefore likely to have slower memory encoding speeds.

4. Memory in competing speech would be worse than in stationary noise because of the stronger masking effect of competing speech.

5. Overall memory performance would be better for participants with higher working memory capacity in the presence of noise reduction. This effect should be more apparent for late list items presented with competing speech babble.

Twenty-six native Swedish-speaking individuals with moderate to moderately-severe, high-frequency sensorineural hearing loss participated in the authors’ study. Prior to commencement of the study, participants were tested to ensure that they had age-appropriate cognitive performance. A battery of tests was administered and results were comparable to previously reported performance for their age group (Ronnberg, 1990).

Two tests were administered to study participants. First, a reading span test evaluated working memory capacity.  Participants were presented with a total of 24 three-word sentences and sub-lists of 3, 4 and 5 sentences were presented in ascending order. Participants were asked to judge whether the sentences were sensible or nonsense. At the end of each sub-list of sentences, listeners were prompted to recall either the first or final words of each sentence, in the order in which they were presented. Tests were scored as the total number of items correctly recalled.

The second test was a sentence-final word identification and recall (SWIR) test, consisting of 140 everyday sentences from the Swedish Hearing In Noise Test (HINT; Hallgren et al, 2006). This test involved two different tasks. The first was an identification task in which participants were asked to report the final word of each sentence immediately after listening to it.  The second task was a free recall task; after reporting the final word of the eighth sentence of the list, they were asked to recall all the words that they had previously reported. Three of seven tested conditions included variations of noise reduction algorithms, ranging from one similar to those implemented in modern hearing aids to an ‘ideal’ noise reduction algorithm.

Prior to the main analyses of working memory and recall performance, two sets of groups were created based on reading span scores, using two different grouping methods. In the first set, two groups were created by splitting the group at the median score so that 13 individuals were in a high reading span group and the remaining 13 were in a low reading span group. In the second set, participants who scored in the mid-range on the reading span test were excluded from the analysis, creating High reading span and Low reading span groups of 10 participants each. There was no significant difference between groups based on age, pure tone average or word identification performance, in any of the noise conditions. Overall reading span scores for participants in this study were comparable to previously reported results (Lunner, 2003; Foo, 2007).

Also prior to the main analysis, the SWIR results were analyzed to compare noise reduction and ideal noise reduction conditions. There was no significant difference between noise reduction and ideal noise reduction conditions in the identification or free recall tasks, nor was there an interaction of noise reduction condition with reading span score. Therefore, only the noise reduction condition was considered in the subsequent analyses.

The relationship between reading span score (representing working memory capacity) and SWIR recall was examined for all the test conditions. Reading span score correlated with overall recall performance in all conditions but one. When recall was analyzed as a function of list position (beginning or final), reading span scores correlated significantly with beginning (primacy) positions in quiet and most noise conditions. There was no significant correlation between overall reading span scores and items in final (recency) position in any of the noise conditions.

There were significant main effects for noise, list position and reading span group. In other words, when noise reduction was implemented, the negative effects of noise were lessened. There was a recency effect, in that performance was better for late list positions than for early list positions. Overall, the high reading span groups scored better than the low reading span groups, for both median-split and mid-range exclusion groups. The high reading span groups showed improved recall with noise reduction, whereas the low reading span groups exhibited no change in performance with noise reduction versus quiet.  The use of four-talker babble had a negative effect on late list positions, but did not affect items in other positions, suggesting that four-talker babble disrupted working memory more than steady-state noise. These analyses supported hypotheses 1, 2, 3 and 5, indicating that noise adversely affects memory performance (1), that noise reduction and list position interact with this effect (2,3) especially for individuals with high working memory capacity (5).

The results also supported hypothesis 4, which suggested that competing speech babble would affect memory performance more than steady state noise. Recall performance was significantly better in the presence of steady-state noise than it was in 4-talker babble. Though there was no significant effect of noise reduction overall, high reading span participants once again outperformed low reading span participants with noise reduction.

In summary, the results of this study determined that noise had an adverse effect on recall, but that this effect was mildly mitigated by the use of noise reduction. Four-talker babble was more disruptive to recall performance than was steady-state noise. Recall performance was better for individuals with higher working memory capacity. These individuals also demonstrated more of a benefit from noise reduction than did those with lower working memory capacity.

Recall performance is better in quiet conditions than in noise because presumably fewer cognitive resources are required to encode the speech input (Murphy, et al., 2000). Ng and her colleagues suggest that noise reduction helps to perceptually segregate speech from noise, allowing the speech input to be matched to stored lexical representations with less cognitive demand. So, noise reduction may at least partially reverse the negative effect of noise on working memory.

Competing speech babble is more likely to be cognitively demanding than steady-state noise (such as an air conditioner) because it contains meaningful information that is more distracting and harder to separate from the speech of interest (Sorqvist & Ronnberg, 2012). Not only is the speech signal of interest degraded by the presence of competing sound and therefore harder to encode, but additional cognitive resources are required to inhibit the unwanted or irrelevant linguistic information (Macken, 2009).  Because competing speech puts more demands on cognitive resources, it is more potentially disruptive than steady-state noise to perception of the speech signal of interest.

Unfortunately, much of the background noise encountered by hearing aid wearers is competing speech. The classic example of the cocktail party illustrates one of the most challenging situations for hearing-impaired individuals, in which they must try to attend to a proximal conversation while ignoring multiple conversations surrounding them. The results of this study suggest that noise reduction may be more useful in these situations for listeners with better working memory capacity; however, noise reduction should still be considered for all hearing aid users, with comprehensive follow-up care to make adjustments for individuals who are not functioning well in noisy conditions. Noise reduction may generally alleviate perceived effort or annoyance, allowing a listener to be more attentive to the speech signal of interest or to remain in a noisy situation that would otherwise be uncomfortable or aggravating.

More research is needed on the effects of noise, noise reduction and advanced signal processing on listening effort and memory in everyday situations. It is likely that performance is affected by numerous variables of the hearing aid, including compression characteristics, directionality, noise reduction, as well as the automatic implementation or adjustment of these features. These variables in turn combine with user-related characteristics such as age, degree of hearing loss, word recognition ability, cognitive capacity and more.

References

Foo, C., Rudner, M., & Ronnberg, J. (2007). Recognition of speech in noise with new hearing instrument compression release settings requires explicit cognitive storage and processing capacity. Journal of the American Academy of Audiology 18, 618-631.

Hallgren, M., Larsby, B. & Arlinger, S. (2006). A Swedish version of the hearing in noise test (HINT) for measurement of speech recognition. International Journal of Audiology 45, 227-237.

Hick, C. B., & Tharpe, A. M. (2002). Listening effort and fatigue in school-age children with and without hearing loss. Journal of Speech Language and Hearing Research 45, 573–584.

Humes, L. (2007). The contributions of audibility and cognitive factors to the benefit provided by amplified speech to older adults. Journal of the American Academy of Audiology 18, 590-603.

Lunner, T. (2003). Cognitive function in relation to hearing aid use. International Journal of Audiology 42, (Suppl. 1), S49-S58.

Lunner, T., Rudner, M. & Ronnberg, J. (2009). Cognition and hearing aids. Scandinavian Journal of Psychology 50, 395-403.

Macken, W.J., Phelps, F.G. & Jones, D.M. (2009). What causes auditory distraction? Psychonomic Bulletin and Review 16, 139-144.

McCoy, S.L., Tun, P.A. & Cox, L.C. (2005). Hearing loss and perceptual effort: downstream effects on older adults’ memory for speech. Quarterly Journal of Experimental Psychology A, 58, 22-33.

Picou, E.M., Ricketts, T.A. & Hornsby, B.W.Y. (2013). How hearing aids, background noise and visual cues influence objective listening effort. Ear and Hearing 34 (5).

Ronnberg, J. (2003). Cognition in the hearing impaired and deaf as a bridge between signal and dialogue: a framework and a model. International Journal of Audiology 42 (Suppl. 1), S68-S76.

Ronnberg, J., Rudner, M. & Foo, C. (2008). Cognition counts: A working memory system for ease of language understanding (ELU). International Journal of Audiology 47 (Suppl. 2), S99-S105.

Rudner, M., Ronnberg, J. & Lunner, T. (2011). Working memory supports listening in noise for persons with hearing impairment. Journal of the American Academy of Audiology 22, 156-167.

Sorqvist, P. & Ronnberg, J. (2012). Episodic long-term memory of spoken discourse masked by speech: What role for working memory capacity? Journal of Speech Language and Hearing Research 55, 210-218.