- 1NIHR Nottingham Hearing Biomedical Research Unit, Otology and Hearing Group, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK
- 2Nottingham University Hospitals NHS Trust, Nottingham, UK
Auditory training (AT) helps compensate for degradation in the auditory signal. A series of three high-quality training studies are discussed, which include, (i) a randomized controlled trial (RCT) of phoneme discrimination in quiet that trained adults with mild hearing loss (n = 44), (ii) a repeated measures study that trained phoneme discrimination in noise in hearing aid (HA) users (n = 30), and (iii) a double-blind RCT that directly trained working memory (WM) in HA users (n = 57). AT resulted in generalized improvements in measures of self-reported hearing, competing speech, and complex cognitive tasks that all index executive functions. This suggests that for AT related benefits, the development of complex cognitive skills may be more important than the refinement of sensory processing. Furthermore, outcome measures should be sensitive to the functional benefits of AT. For WM training, lack of far-transfer to untrained outcomes suggests no generalized benefits to real-world listening abilities. We propose that combined auditory-cognitive training approaches, where cognitive enhancement is embedded within auditory tasks, are most likely to offer generalized benefits to the real-world listening abilities of adults with hearing loss.
Listening and Communication in Adverse Conditions
It is widely accepted that understanding speech in background noise is the most common problem for people with hearing loss (Vermiglio et al., 2012; Humes et al., 2013), as characterized by the typical statement “I can hear but I cannot understand what is being said.” In addition to a loss of hearing sensitivity, there may be additional deficits of temporal and spectral processing that contribute to listening difficulties (Hopkins and Moore, 2011). Furthermore, there is mounting evidence that non-sensory factors such as cognition, motivation, and context, play an important role in both listening to speech (one-way interaction process) and communication (bi-directional interaction; Kiessling et al., 2003; Pichora-Fuller and Singh, 2006; Rudner et al., 2011). This is particularly evident for older listeners (Gordon-Salant, 2014; Moore et al., 2014).
The role of cognition becomes more apparent when communicating in adverse conditions, such as when listening to speech in fluctuating background noise or competing talkers (Akeroyd, 2008; Humes and Dubno, 2010). Speech in noise performance is associated with cognition, and the role of cognition becomes increasingly important as the complexity of the listening task increases (Heinrich et al., 2015). For a listener to be able to understand a specific speech source amongst a background of other talkers, the auditory streams or sound sources need to be simultaneously attended to and monitored, and attention may need to be switched between them (Gatehouse and Noble, 2004; Shinn-Cunningham and Best, 2008). This requires the engagement of executive processes that regulate, control, and manage other cognitive processes, such as attention and working memory (WM; Chan et al., 2008).
Cognition and the Clinical Management of People with Hearing Loss
The role of cognition has implications for the clinical management of people with hearing loss. Hearing aids (HAs) are the main intervention for people with hearing loss and have undergone significant advances in digital technology over the last two decades. Whilst satisfaction with HAs has improved since the 1990s (Kochkin, 2010), users often continue to encounter difficulties in challenging listening conditions (Johnson and Dillon, 2011). Early studies with HA users showed an association between behavioral and subjective HA outcomes and measures of cognitive skills (Gatehouse et al., 2003; Lunner, 2003). Furthermore, those with better cognitive skills were better able to take advantage of advanced signal processing strategies, such as fast-acting compression (Foo et al., 2007; Lunner and Sundewall-Thorén, 2007). Other processing strategies, such as noise reduction algorithms, have also been shown to reduce effortful listening and free up cognitive resources to be used for other tasks (Sarampalis et al., 2009).
When considering interventions to aid communication in people with hearing loss, HAs alone are not the only option. Other rehabilitation approaches include patient-centered education, counseling, and auditory perceptual training, which can help impaired listeners compensate for degradation in the auditory signal and improve communication (Sweetow and Sabes, 2006). This article focusses on developments in auditory training (AT), and more recently cognitive training, and how this may improve speech perception, cognition and ultimately, everyday communication in adults with hearing loss, offering a view to future research directions.
Auditory Training
Auditory perceptual training can be described as teaching the brain to listen through active engagement with sounds, whereby listeners typically learn to make perceptual distinctions between sounds presented systematically (Schow and Nerbonne, 2006). Training on perceptual distinctions implies a primarily bottom-up approach to training whereby the individual actively listens to auditory stimuli (e.g., tones, phonemes, words) to improve listening and communication. This is reflected in the literature where traditionally, training studies have focussed primarily on the sensory refinement of auditory stimuli to improve speech perception (Fu et al., 2004; Stecker et al., 2006). But as Schow and Nerbonne’s (2006) definition suggests, the role of top–down cognitive processes is implicit in AT and subsequent learning. This has been demonstrated by training on non-auditory tasks, such as visual discrimination or visuospatial tasks, and auditory tasks with identical stimuli, resulting in learning in the auditory domain (Amitay et al., 2006). Such results imply that learning is mediated by top–down processes. Thus, AT may provide a means to improve both auditory and cognitive processes in people with hearing loss in order to improve listening and communication in everyday life (Pichora-Fuller and Levitt, 2012).
Efficacy of Auditory Training
The turn of the last decade saw a proliferation of individualized, computer-based auditory training research. Basic research sought to better understand the underlying principles and biological mechanisms of AT in normally hearing listeners (e.g., Tremblay, 2007; de Boer and Thornton, 2008; Wright and Zhang, 2009; Song et al., 2011). In addition, translational research sought to establish the efficacy of AT to improve outcomes for people with hearing loss, including users of HAs and cochlear implants (for review, see Henshaw and Ferguson, 2013a). Efficacy of AT can be assessed by (i) improvements in performance for the trained task (on-task learning), (ii) improvements in performance on the untrained task (off-task, generalized, or transfer of learning), (iii) retention of learning for a period after training ceases, and (iv) adherence of the individual with training. This article concentrates on (i)–(iii). Motivations of individuals to participate in, engage with, and adhere to home-delivered training are discussed elsewhere (Henshaw et al., in review; Ferguson and Henshaw, in press).
Our recent systematic review on the efficacy of computer-based auditory training as a clinical intervention for adults with hearing loss summarized the evidence base between 1996 and 2011 and included 13 studies (Henshaw and Ferguson, 2013a). The review concluded that, where reported, on-task learning always occurred in those with mild-moderate hearing loss (whether HA users or not) for a range of training stimuli including phonemes, words, and sentences (e.g., Burk et al., 2006; Stecker et al., 2006; Sweetow and Sabes, 2006). The evidence for on-task learning in cochlear implants users generally followed this trend (e.g., Fu et al., 2004; Tyler et al., 2010; Oba et al., 2011) with the exception of Stacey et al. (2010). However, the evidence for generalization of learning to untrained measures was mixed. Although generalized improvements were shown for speech intelligibility (11/13 articles), self-report of communication (1/2), and cognition (1/1), the improvements were variable in that reported improvements were inconsistent across studies, and the magnitude of improvement was small and not robust. It was notable that all the studies had at least one outcome measure on speech intelligibility, yet different studies rarely used the same measure. Only two studies measured self-reported communication as a means to tap into perceived real-world benefits of training, and just one study measured cognition. The quality of the evidence for included studies was very-low to moderate. Reasons for this included failure to include a control group, and a lack of randomization, power calculation, and participant or tester blinding.
Our Approach to Auditory Training
Following on from the systematic review, we sought to address many of the study quality limitations of the existing published evidence with a series of three high-quality auditory and cognitive training studies that aimed to assess benefits to speech perception, cognition, and self-reported communication in people with mild-moderate hearing loss. The study methods are outlined in Table 1. Outcome measures are shown in Table 2, and are described in more detail in the original articles.
Across all three studies, hearing loss was described by the better-ear pure-tone threshold averaged across octave frequencies 0.5–4 kHz as either mild (21–40 dB HL), or moderate (41–70 dB HL). Participants were aged 50–74 years old, and training was home-delivered either via loan laptops (AT studies) or via the internet (working memory training). Each study included a control period that allowed for the examination of procedural learning (test–retest) effects on outcomes (Mcarthur, 2007).
Auditory Training Study 1: Training Improves Outcomes that Index Executive Function
The study was a randomized controlled trial, whereby a 4-week phoneme discrimination training program was performed for the Immediate Trained (IT) group at weeks 1–4, and a Delayed Trained (DT) group at weeks 5–8 provided a control comparison. Outcome measure assessments were obtained for the IT and DT groups at weeks 0, 4, and 8, and for the DT group at 12 weeks (Ferguson et al., 2014).
Results showed significant and robust on-task learning for all trained phoneme continua. The on-task learning and retention of on-task learning results were consistent with studies in the systematic review. However, from a clinical perspective the value of training as an intervention lies in the generalization of task-specific learning to functional benefits in real-world listening. A summary of the results from the untrained outcome measures is shown in Table 2, whereby tests and self-report questions were classified as complex if they indexed executive processes, and simple if they did not. Details of analysis using Multivariate Analysis of Variance is reported elsewhere (Ferguson et al., 2014). As we were also interested in the clinical effects of AT as an intervention, Cohen’s d is reported where effect size was interpreted as small (0.2), moderate (0.5), and large (0.8) (Cohen, 1988).
For the speech perception in noise tests that used energetic masking, there were no significant training-related improvements. For tests of cognition, there were no pre–post training improvements for the simple tasks, including simple-span WM measure (digit span) and the single attention test [Test of Everyday Attention (TEA) subtest 6] for either the intervention or control groups. However, for the complex tasks that indexed executive processes, there were significant pre–post training improvements shown for divided attention (TEA subtest 7) and the updating of WM (visual letter monitoring, VLM). For VLM there was a larger effect shown for the faster, more challenging presentation (one letter per second, d = 0.50) compared to the slower presentation (one letter every 2 s, d = 0.34).
For self-report of communication using the Glasgow Hearing Aid Benefit Profile (GHABP), there was a significant effect of training on the overall score for activity limitation (previously termed hearing disability) with a moderate effect size, suggesting real-world benefits were perceived by participants. A secondary analysis of the individual situations of the GHABP revealed an interesting insight in that no significant pre–post training improvements for the simple listening situations, such as ‘having a conversation with one other person when there is no background noise’ were shown. However, there was a significant effect of training for the most challenging listening situation ‘having a conversation with several people in a group.’ This requires the listener to constantly monitor the conversation, switch, and update attention (i.e., engage executive processes), whilst the other situations do not. These results were supported by qualitative analysis of open-ended questions and focus groups from participants who reported that the main benefits of the training were increased concentration and focus in everyday listening (Henshaw et al., in review). Across all measures where there were significant effects of training, these were retained 4 weeks post-training. Finally, in the participants where there were improvements in the GHABP measures, there was a significant correlation between self-report and divided attention (r = 0.79, p < 0.001), suggesting that improvements in self-report were not a ‘placebo’ effect of undertaking the training program.
These results suggest that outcome measures need to be appropriately complex and challenging to be sensitive to the effects of AT, and taken together, the value of AT to mediate cognitive skills may be more important than the improvement of sensory skills for communication in everyday life.
This led us to reconsider the non-significant speech perception results. Given that AT showed an improvement in the cognitive functions that index executive processes, we made the hypothesis that training-related improvements would be evident in informational masked speech perception tests (e.g., competing speech) that engage executive processes (Shinn-Cunningham, 2008), rather than the energetically masked speech tests that were included in this study, which primarily assess audibility. This was explored in study 2.
Auditory Training Study 2: Training Improves Competing Speech and Dual-Task Performance
This study used a within-participant repeated measures design with an initial 1-week control period, followed by a 1-week training period (Henshaw and Ferguson, 2014). The training duration was 3.5 h, just over half that of the previous study, as the majority of the phoneme discrimination learning had taken place by this time. The modified co-ordinate response measure (MCRM) used a single female talker target and single male talker masker, presented simultaneously. The dual-task included a digit recall task (secondary), which flanked a word-in-noise repetition test (primary), presented at three signal-to-noise (SNR) levels (quiet, 0 dB and -4 dB).
Participants demonstrated significant on-task learning for the trained auditory task. Results for the untrained measures are shown in Table 2. For competing speech (MCRM), there was a significant pre–post training improvement with a moderate effect size and no improvement shown for the control (no-training) period. This confirmed our hypothesis and suggests that it is important to use appropriate speech measures that tap into the underpinning mechanisms of benefit provided by AT.
For the dual task, there was no effect of training for the easiest (quiet) or most difficult (-4 dB SNR) test conditions. However, there was a significant pre–post training improvement for the intermediate level of difficulty (0 dB SNR), with a large effect size. This suggests that the HA users in this study were better able to allocate their available cognitive resources between the speech and memory tasks post-training, and suggests that outcome measures need to be appropriately challenging in order to be sensitive to post-training improvements.
Given these results, we asked the question: “Could training cognition directly offer a more direct route to benefit for people with hearing loss?.”
Working Memory Training for People With Hearing Loss
We used a WM training program (Cogmed RM) comprising verbal and visuospatial WM and memory storage tasks. Published studies of Cogmed RM have shown post-training improvements in untrained tasks of attention and self-report of cognitive function in younger and older adults (Brehmer et al., 2012), and improvements in sentence repetition for children with cochlear implants (Kronenberger et al., 2011).
Working Memory Training: Training Results in Near-Transfer but not Far-Transfer of Learning
A registered clinical trial of 57 existing HA users with mild-moderate hearing loss assessed benefits to speech perception, self-reported communication, and cognition (for protocol, including outcome measures, see Henshaw and Ferguson, 2013b). In addition to assessing generalization to untrained tasks, we examined how far along the spectrum of near-transfer (e.g., outcome is close to the trained task) to far-transfer (e.g., untrained task in a different modality) any improvements occurred (Perkins and Salomon, 1992). Results (not yet published), showed near-transfer (i.e., improvements in an untrained WM task), but no far-transfer (e.g., speech perception) of training-related improvements, despite a longer training duration than for the AT studies. These results are consistent with the cognitive neuroscience literature, which shows that WM training can enhance WM tasks that share similar structural features (Thompson et al., 2013), however, training does not generalize to enhancement of the broader underlying cognitive constructs (Melby-Lervag and Hulme, 2013). It has been suggested that training-related improvements in trained WM tasks may be mediated by specific strategies, such as chunking or grouping (Dunning and Holmes, 2014), which may limit the broader applicability to benefit cognitive constructs underpinning successful communication for HA users.
Auditory-Cognitive Training: Joined-up Listening and Thinking
Recent studies of an auditory-based cognitive training program that combines auditory perceptual training with increased memory demands (Brain Fitness; Posit Science) have demonstrated generalized improvements in non-trained tests of memory, attention, and speed of processing in older adults (Smith et al., 2009), in addition to improved neural timing and speech perception in noise (Anderson et al., 2013a,b). Similar results for a ‘hybrid’ training program comprising exercises of speech and cognition [Listening and Communication Enhancement (LACE), Sweetow and Sabes, 2006] trialed in mainly HA users, showed generalized improvements in speech in noise, auditory WM and speed of processing, in addition to improvements in self-report of communication difficulties. However, it is not clear from these studies which element of the training program is responsible for the transfer of learning.
Future Directions
Following on from our own research and developments from the current literature, we propose that benefits of training for people with hearing loss in terms of improved speech understanding in adverse conditions may be best achieved if an integrated auditory-cognitive training approach is taken. This approach would serve to target the cognitive processes that underpin speech perception within a speech task, rather than training specific cognitive tasks that are far-removed from speech. One benefit of this approach is that the degree of transfer required to realize real-world benefit is substantially reduced. Furthermore, the nature of the speech task is more readily perceived as relevant to individuals in terms of their hearing difficulties, which is likely to aid motivation for adherence (Henshaw et al., in review).
Finally, a recent study has shown a dynamic relationship between WM capacity and speech recognition in the first 6 months of HA use with WM playing a greater role in speech perception initially, whereas after 6 months hearing sensitivity is more influential (Ng et al., 2014). Based on the Ease of Language Understanding model (Ronnberg et al., 2013), the authors suggest that as the unfamiliar processed phonological representations become more familiar with time, often referred to as acclimatization (Arlinger et al., 1996), there is a reduced requirement to use WM capacity for speech perception. However, the role of cognition in the acclimatization process is likely to extend beyond WM, and may call upon executive processes required for understanding speech. We are currently examining this in a longitudinal study of first-time HA users. Having identified which cognitive processes are important in acclimatization we aim to use a relevant auditory-cognitive training paradigm to minimize the difficulties faced in the early days of HA use.
Author Contributions
MF and HH designed the studies. MF and HH analyzed and interpreted the data. MF wrote the manuscript. MF and HH contributed to manuscript revisions and critical discussions. Both authors approved the final version of the manuscript for publication. Both authors agree to be accountable for all aspects of the work and in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgments
The authors would like to thank our colleagues Dave Moore, Daniel Clark, Holly Thomas, Ashana Tittle, and Mark Edmondson-Jones for their contributions to this research. Cogmed is a registered trademark of Pearson, Inc. or its affiliate(s). All rights reserved. This paper presents independent research funded by the National Institute for Health Research (NIHR) Biomedical Research Unit Programme. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health.
References
Akeroyd, M. A. (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. Int. J. Audiol. 47, S53–S71. doi: 10.1080/14992020802301142
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Amitay, S., Irwin, A., and Moore, D. R. (2006). Discrimination learning induced by training with identical stimuli. Nat. Neurosci. 9, 1446–1448. doi: 10.1038/nn1787
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Anderson, S., White-Schwoch, T., Choi, H. J., and Kraus, N. (2013a). Training changes processing of speech cues in older adults with hearing loss. Front. Syst. Neurosci. 7:97. doi: 10.3389/fnsys.2013.00097
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Anderson, S., White-Schwoch, T., Parbery-Clark, A., and Kraus, N. (2013b). Reversal of age-related neural timing delays with training. Proc. Natl. Acad. Sci. U.S.A. 110, 4357–4362.
Arlinger, S., Gatehouse, S., Bentler, R., Byrne, D., Cox, R., Dirks, D., et al. (1996). Report of the eriksholm workshop on auditory deprivation and acclimatization. Ear Hear. 17(Suppl. 3), 87S–98S. doi: 10.1097/00003446-199617031-00009
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Brehmer, Y., Westerberg, H., and Backman, L. (2012). Working-memory training in younger and older adults: training gains, transfer, and maintenance. Front. Hum. Neurosci. 6:63. doi: 10.3389/fnhum.2012.00063
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Burk, M. H., Humes, L. E., Amos, N. E., and Strauser, L. E. (2006). Effect of training on word-recognition performance in noise for young normal-hearing and older hearing-impaired listeners. Ear Hear. 27, 263–278. doi: 10.1097/01.aud.0000215980.21158.a2
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Chan, R. C. K., Shum, D., Toulopoulou, T., and Chen, E. Y. H. (2008). Assessment of executive functions: review of instruments and identification of critical issues. Arch. Clin. Neuropsychol. 23, 201–216. doi: 10.1016/j.acn.2007.08.010
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, 2nd Edn. Hillsdale, NJ: Lawrence Earlbaum Associates.
de Boer, J., and Thornton, A. R. D. (2008). Neural correlates of perceptual learning in the auditory brainstem: efferent activity predicts and reflects improvement at a speech-in-noise discrimination task. J. Neurosci. 28, 4929–4937. doi: 10.1523/JNEUROSCI.0902-08.2008
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Dunning, D. L., and Holmes, J. (2014). Does working memory training promote the use of strategies on untrained working memory tasks? Mem. Cognit. 42, 854–862.
Ferguson, M. A., and Henshaw, H. (in press). Computer and internet interventions to optimise listening and learning for people with hearing loss: accessibility, use and adherence. Am. J. Audiol.
Ferguson, M. A., Henshaw, H., Clark, D., and Moore, D. (2014). Benefits of phoneme discrimination training in a randomized controlled trial of 50–74 year olds with mild hearing loss. Ear Hear. 35, e110–e121. doi: 10.1097/AUD.0000000000000020
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Foo, C., Rudner, M., Ronnberg, J., and Lunner, T. (2007). Recognition of speech in noise with new hearing instrument compression release settings requires explicit cognitive storage and processing capacity. J. Am. Acad. Audiol. 18, 618–631. doi: 10.3766/jaaa.18.7.8
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Fu, Q. J., Galvin, J., Wang, X., and Nogaki, G. (2004). Effects of auditory training on adult cochlear implant patients: a preliminary report. Cochlear Implants Int. 5, 84–90. doi: 10.1179/cim.2004.5.Supplement-1.84
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Gatehouse, S. (1999). Glasgow hearing aid benefit profile: derivation and validation of client-centred outcome measures for hearing aid services. J. Am. Acad. Audiol. 10, 80–103.
Gatehouse, S., Naylor, G., and Elberling, C. (2003). Benefits from hearing aids in relation to the interaction between the user and the environment. Int. J. Audiol. 42, S77–S85. doi: 10.3109/14992020309074627
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Gatehouse, S., and Noble, W. (2004). The Speech, Spatial and Qualities of Hearing Scale (SSQ). Int. J. Audiol. 43, 85–99. doi: 10.1080/14992020400050014
Gordon-Salant, S. (2014). “Aging, hearing loss, and speech recognition: stop shouting, i can’t understand you,” in Perspectives on Auditory Research, eds A. N. Popper and R. R. Fay (New York, NY: Springer), 211–228.
Hazan, V., Messaoud-Galusi, S., Rosen, S., Nouwens, S., and Shakespeare, B. (2009). Speech perception abilities of adults with dyslexia: is there any evidence for a true deficit? J. Speech Lang. Hear. Res. 52, 1510–1529. doi: 10.1044/1092-4388(2009/08-0220)
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Heinrich, A., Henshaw, H., and Ferguson, M. A. (2015). The relationship of speech intelligibility with hearing sensitivity, cognition, and perceived hearing difficulties varies for different speech perception tests. Front. Psychol. 6:782. doi: 10.3389/fpsyg.2015.00782
Henshaw, H., and Ferguson, M. A. (2013a). Efficacy of individual computer-based auditory training for people with hearing loss: a systematic review of the evidence. PLoS ONE 8:e62836. doi: 10.1371/journal.pone.0062836
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Henshaw, H., and Ferguson, M. A. (2013b). Working memory training for adult hearing aid users: study protocol for a double-blind randomized active controlled trial. Trials 14:417.
Henshaw, H., and Ferguson, M. A. (2014). “Assessing the benefits of auditory training to real-world listening: identifying appropriate and sensitive outcomes,” in Proceedings of ISAAR 2013: Auditory Plasticity – Listening with the Brain. 4th symposium on Auditory and Audiological Research, eds T. Dau, S. Santurette, J. C. Dalsgaard, L. Trangjaerg, T. Andersen, and T. Poulsen (Nyborg: The Danavox Jubilee Foundation).
Hopkins, K., and Moore, B. C. (2011). The effects of age and cochlear hearing loss on temporal fine structure sensitivity, frequency selectivity, and speech reception in noise. J. Acoust. Soc. Am. 130, 334–349. doi: 10.1121/1.3585848
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Howard, C. S., Munro, K. J., and Plack, C. J. (2010). Listening effort at signal-to-noise ratios that are typical of the school classroom. Int. J. Audiol. 49, 928–932. doi: 10.3109/14992027.2010.520036
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Humes, L. E., and Dubno, J. R. (2010). “Factors affecting speech understanding in older adults,” in The Aging Auditory System, eds S. Gordon-Salant, R. D. Frisna, A. N. Popper, and R. R. Fay (New York, NY: Springer), 211–257.
Humes, L. E., Kidd, G. R., and Lentz, J. J. (2013). Auditory and cognitive factors underlying individual differences in aided speech-understanding among older adults. Front. Syst. Neurosci. 7:55. doi: 10.3389/fnsys.2013.00055
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Johnson, E. E., and Dillon, H. (2011). A comparison of gain for adults from generic hearing aid prescriptive methods: impacts on predicted loudness, frequency bandwidth, and speech intelligibility. J. Am. Acad. Audiol. 22, 441–459. doi: 10.3766/jaaa.22.7.5
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Kiessling, J., Pichora-Fuller, M. K., Gatehouse, S., Stephens, D., Arlinger, S., Chisolm, T., et al. (2003). Candidature for and delivery of audiological services: special needs of older people. Int. J. Audiol. 42, S92–S101. doi: 10.3109/14992020309074650
Kochkin, S. (2010). MarkeTrak VIII: consumer satisfaction with hearing aids is slowly increasing. Hear. J. 63, 19–20. doi: 10.1097/01.HJ.0000366912.40173.76
Kronenberger, W. G., Pisoni, D. B., Henning, S. C., Colson, B. G., and Hazzard, L. M. (2011). Working memory training for children with cochlear implants: a pilot study. J. Speech Lang. Hear. Res. 54, 1182–1196. doi: 10.1044/1092-4388(2010/10-0119)
Lunner, T. (2003). Cognitive function in relation to hearing aid use. Int. J. Audiol. 42, S49–S58. doi: 10.3109/14992020309074624
Lunner, T., and Sundewall-Thorén, E. (2007). Interactions between cognition, compression, and listening conditions: effects on speech-in-noise performance in a two-channel hearing aid. J. Am. Acad. Audiol. 18, 604–617. doi: 10.3766/jaaa.18.7.7
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Mcarthur, G. (2007). Test–retest effects in treatment studies of reading disability: the devil is in the detail. Dyslexia 13, 240–252. doi: 10.1002/dys.355
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Melby-Lervag, M., and Hulme, C. (2013). Is working memory training effctive? A meta- analytic review. Dev. Psychol. 49, 270–291. doi: 10.1037/a0028228
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Millward, K., Moore, D. R., Sohoglu, E., and Amitay, S. (2011). Training speech-in-noise perception in mainstream school children. Int. J. Pediatr. Otorhinolaryngol. 75, 1408–1417. doi: 10.1016/j.ijporl.2011.08.003
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Moore, D. R., Edmondson-Jones, M., Dawes, P., Fortnum, H., Mccormack, A., Pierzycki, R. H., et al. (2014). Relation between speech-in-noise threshold, hearing loss and cognition from 40–69 years of age. PLoS ONE 9:e107720. doi: 10.1371/journal.pone.0107720
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Ng, E. H., Classon, E., Larsby, B., Arlinger, S., Lunner, T., Rudner, M., et al. (2014). Dynamic relation between working memory capacity and speech recognition in noise during the first 6 months of hearing aid use. Trends Hear. 18, 2331216514558688. doi: 10.1177/2331216514558688
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Oba, S. I., Fu, Q., and Galvin, J. J. (2011). Digit training in noise can improve cochlear implant users’ speech understanding in noise. Ear Hear. 32, 573–581. doi: 10.1097/AUD.0b013e31820fc821
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Perkins, D. N., and Salomon, G. (1992). “Transfer of learning,” in International Encyclopedia of Education, 2nd Edn, eds T. Husén and T. Postlethwaite (Oxford: Pergamon Press).
Pichora-Fuller, M. K., and Levitt, H. (2012). Speech comprehension training and auditory and cognitive processing in older adults. Am. J. Audiol. 21, 351–357. doi: 10.1044/1059-0889(2012/12-0025)
Pichora-Fuller, M. K., and Singh, G. (2006). Effects of age on auditory and cognitive processing: implications for hearing aid fitting and audiologic rehabilitation. Trends Amplif. 10, 29–59. doi: 10.1177/108471380601000103
Robertson, I. H., Ward, T., Ridgeway, V., and Nimmo-Smith, I. (1996). The structure of normal human attention: The Test of Everyday Attention. J. Int. Neuropsychol. Soc. 2, 525–534. doi: 10.1017/S1355617700001697
Ronnberg, J., Lunner, T., Zekveld, A., Sorqvist, P., Danielsson, H., Lyxell, B., et al. (2013). The Ease of Language Understanding (ELU) model: theoretical, empirical, and clinical advances. Front. Syst. Neurosci. 7:31. doi: 10.3389/fnsys.2013.00031
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Rudner, M., Rönnberg, J., and Lunner, T. (2011). Working memory supports listening in noise for persons with hearing impairment. J. Am. Acad. Audiol. 22, 156–167. doi: 10.3766/jaaa.22.3.4
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Sarampalis, A., Kalluri, S., Edwards, B., and Hafter, E. (2009). Objective measures of listening effort: effects of background noise and noise reduction. J. Speech Lang. Hear. Res. 52, 1230–1240. doi: 10.1044/1092-4388(2009/08-0111)
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Schow, R., and Nerbonne, M. (2006). Introduction to Audiologic Rehabilitation. Boston, MA: Pearson Education.
Shinn-Cunningham, B. G. (2008). Object-based auditory and visual attention. Trends Cogn. Sci. 12, 182–186. doi: 10.1016/j.tics.2008.02.003
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Shinn-Cunningham, B. G., and Best, V. (2008). Selective attention in normal and impaired hearing. Trends Amplif. 12, 283–299. doi: 10.1177/1084713808325306
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Smith, G. E., Housen, P., Yaffe, K., Ruff, R., Kennison, R. F., Mahncke, H. W., et al. (2009). A cognitive training program based on principles of brain plasticity: results from the Improvement in Memory with Plasticity-based Adaptive Cognitive Training (IMPACT) study. J. Am. Geriatr. Soc. 57, 594–603. doi: 10.1111/j.1532-5415.2008.02167.x
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Smits, C., Kapteyn, T. S., and Houtgast, T. (2004). Development and validation of an automatic speech-in-noise screening test by telephone. Int. J. Audiol. 43, 15–28. doi: 10.1080/14992020400050004
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Song, J. H., Skoe, E., Banai, K., and Kraus, N. (2011). Training to improve hearing speech in noise: biological mechanisms. Cereb. Cortex 22, 1180–1190. doi: 10.1093/cercor/bhr196
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Stacey, P. C., Raine, C. H., O’donoghue, G. M., Tapper, L., Twomey, T., and Summerfield, A. Q. (2010). Effectiveness of computer-based auditory training for adult users of cochlear implants. Int. J. Audiol. 49, 347–356. doi: 10.3109/14992020903397838
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Stecker, G. C., Bowman, G. A., Yund, E. W., Herron, T. J., Roup, C. M., and Woods, D. L. (2006). Perceptual training improves syllable identification in new and experienced hearing aid users. J. Rehabil. Res. Dev. 43, 537–551. doi: 10.1682/JRRD.2005.11.0171
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Sweetow, R. W., and Sabes, J. H. (2006). The need for and development of an Adaptive Listening and Communication Enhancement (LACE) Program. J. Am. Acad. Audiol. 17, 538–558. doi: 10.3766/jaaa.17.8.2
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Thompson, T. W., Waskom, M. L., Garel, K. A., Cardenas-Iniguez, C., Reynolds, G. O., Winter, R., et al. (2013). Failure of working memory training to enhance cognition or intelligence. PLoS ONE 8:e63614. doi: 10.1371/journal.pone.0063614
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Tremblay, K. (2007). Training-related changes in the brain: evidence from human auditory-evoked potentials. Semin. Hear. 28, 120–132. doi: 10.1055/s-2007-973438
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Tyler, R. S., Witt, S. A., Dunn, C. C., and Wang, W. (2010). Initial development of a spatially separated speech-in-noise and localization training program. J. Am. Acad. Audiol. 21, 390–403. doi: 10.3766/jaaa.21.6.4
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Vermiglio, A. J., Soli, S. D., Freed, D. J., and Fisher, L. M. (2012). The relationship between high-frequency pure-tone hearing loss, hearing in noise test (HINT) thresholds, and the articulation index. J. Am. Acad. Audiol. 23, 779–788. doi: 10.3766/jaaa.23.10.4
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Wechsler, D. (1997). Wechsler Adult Intelligence Scale, 3rd Edn. San Antonio, TX: The Psychological Corporation.
Wright, B. A., and Zhang, Y. (2009). A review of the generalization of auditory learning. Philos. Trans. R. Soc. B Biol. Sci. 364, 301–311. doi: 10.1098/rstb.2008.0262
PubMed Abstract | Full Text | CrossRef Full Text | Google Scholar
Keywords: auditory training, hearing loss, working memory, attention, communication, hearing aids, executive function, speech perception
Citation: Ferguson MA and Henshaw H (2015) Auditory training can improve working memory, attention, and communication in adverse conditions for adults with hearing loss. Front. Psychol. 6:556. doi: 10.3389/fpsyg.2015.00556
Received: 11 February 2015; Accepted: 16 April 2015;
Published online: 28 May 2015
Edited by:
Mary Rudner, Linköping University, SwedenReviewed by:
Larry E. Humes, Indiana University Bloomington, USAPaula Clare Stacey, Nottingham Trent University, UK
Copyright © 2015 Ferguson and Henshaw. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Melanie A. Ferguson, NIHR Nottingham Hearing Biomedical Research Unit, Otology and Hearing Group, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Ropewalk House, 113 Ropewalk, Nottingham, NG1 5DU, UK, melanie.ferguson@nottingham.ac.uk