Performance Monitoring and Cognitive Inhibition during a Speech-in-Noise Task in Older Listeners

Abstract

The goal of this study was to examine the effect of hearing loss on theta and alpha electroencephalography (EEG) frequency power measures of performance monitoring and cognitive inhibition, respectively, during a speech-in-noise task. It was hypothesized that hearing loss would be associated with an increase in the peak power of theta and alpha frequencies toward easier conditions compared to normal hearing adults. The shift would reflect how hearing loss modulates the recruitment of listening effort to easier listening conditions. Nine older adults with normal hearing (ONH) and 10 older adults with hearing loss (OHL) participated in this study. EEG data were collected from all participants while they completed the words-in-noise task. It hypothesized that hearing loss would also have an effect on theta and alpha power. The ONH group showed an inverted U-shape effect of signal-to-noise ratio (SNR), but there were limited effects of SNR on theta or alpha power in the OHL group. The results of the ONH group support the growing body of literature showing effects of listening conditions on alpha and theta power. The null results of listening condition in the OHL group add to a smaller body of literature, suggesting that listening effort research conditions should have near ceiling performance.

Keywords

alpha power - EEG - effortful listening - theta power - word recognition

Disclaimer

The content of this article does not represent the views of the U.S. government or the Department of Veterans Affairs.

Publikationsverlauf

Artikel online veröffentlicht:
28. April 2023

• References

• 1 Speech understanding and aging. Working Group on Speech Understanding and Aging. Committee on Hearing, Bioacoustics, and Biomechanics, Commission on Behavioral and Social Sciences and Education, National Research Council. J Acoust Soc Am 1988; 83 (03) 859-895
• 2 Anderson S, Parbery-Clark A, Yi HG, Kraus N. A neural basis of speech-in-noise perception in older adults. Ear Hear 2011; 32 (06) 750-757
• 3 Plomp R. A signal-to-noise ratio model for the speech-reception threshold of the hearing impaired. J Speech Hear Res 1986; 29 (02) 146-154
• 4 Pichora-Fuller MK. Rehabilitative audiology: using the brain to reconnect listeners with impaired ears to their acoustic ecologies. J Am Acad Audiol 2007; 18 (07) 536-538
• 5 Pichora-Fuller MK, Kramer SE, Eckert MA. et al. Hearing impairment and cognitive energy: the Framework for Understanding Effortful Listening (FUEL). Ear Hear 2016; 37 (Suppl. 01) 5S-27S
• 6 Rönnberg J, Lunner T, Zekveld A. et al. The Ease of Language Understanding (ELU) model: theoretical, empirical, and clinical advances. Front Syst Neurosci 2013; 7: 31
• 7 Alhanbali S, Dawes P, Millman RE, Munro KJ. Measures of listening effort are multidimensional. Ear Hear 2019; 40 (05) 1084-1097
• 8 Kestens K, Degeest S, Keppler H. The effect of cognition on the aided benefit in terms of speech understanding and listening effort obtained with digital hearing aids: a systematic review. Am J Audiol 2021; 30 (01) 190-210
• 9 Alhanbali S, Munro KJ, Dawes P, Carolan PJ, Millman RE. Dimensions of self-reported listening effort and fatigue on a digits-in-noise task, and association with baseline pupil size and performance accuracy. Int J Audiol 2021; 60 (10) 762-772
• 10 Woodcock RW, McGrew KS, Mather N. Woodcock-Johnson Tests of Achievement. Riverside Publishing; 2001
• 11 Rönnberg N, Stenfelt S, Rudner M. Testing listening effort for speech comprehension using the individuals' cognitive spare capacity. Audiology Res 2011; 1 (01) e22
• 12 Smith SL, Pichora-Fuller MK, Alexander G. Development of the word auditory recognition and recall measure: a working memory test for use in rehabilitative audiology. Ear Hear 2016; 37 (06) e360-e376
• 13 Gagné JP, Besser J, Lemke U. Behavioral assessment of listening effort using a dual-task paradigm. Trends Hear 2017; 21: 2331216516687287
• 14 Billings CJ, Penman TM, McMillan GP, Ellis EM. Electrophysiology and perception of speech in noise in older listeners: effects of hearing impairment and age. Ear Hear 2015; 36 (06) 710-722
• 15 Fiedler L, Seifi Ala T, Graversen C, Alickovic E, Lunner T, Wendt D. Hearing aid noise reduction lowers the sustained listening effort during continuous speech in noise—a combined pupillometry and EEG study. Ear Hear 2021; 42 (06) 1590-1601
• 16 Wöstmann M, Alavash M, Obleser J. Alpha oscillations in the human brain implement distractor suppression independent of target selection. J Neurosci 2019; 39 (49) 9797-9805
• 17 Cavanagh JF, Frank MJ. Frontal theta as a mechanism for cognitive control. Trends Cogn Sci 2014; 18 (08) 414-421
• 18 Wisniewski MG, Thompson ER, Iyer N, Estepp JR, Goder-Reiser MN, Sullivan SC. Frontal midline θ power as an index of listening effort. Neuroreport 2015; 26 (02) 94-99
• 19 Wisniewski MG, Thompson ER, Iyer N. Theta- and alpha-power enhancements in the electroencephalogram as an auditory delayed match-to-sample task becomes impossibly difficult. Psychophysiology 2017; 54 (12) 1916-1928
• 20 Obleser J, Weisz N. Suppressed alpha oscillations predict intelligibility of speech and its acoustic details. Cereb Cortex 2012; 22 (11) 2466-2477
• 21 Wöstmann M, Herrmann B, Wilsch A, Obleser J. Neural alpha dynamics in younger and older listeners reflect acoustic challenges and predictive benefits. J Neurosci 2015; 35 (04) 1458-1467
• 22 Dimitrijevic A, Smith ML, Kadis DS, Moore DR. Cortical alpha oscillations predict speech intelligibility. Front Hum Neurosci 2017; 11: 88
• 23 Paul BT, Chen J, Le T, Lin V, Dimitrijevic A. Cortical alpha oscillations in cochlear implant users reflect subjective listening effort during speech-in-noise perception. PLoS One 2021; 16 (07) e0254162
• 24 Strauß A, Wöstmann M, Obleser J. Cortical alpha oscillations as a tool for auditory selective inhibition. Front Hum Neurosci 2014; 8: 350
• 25 Petersen EB, Wöstmann M, Obleser J, Stenfelt S, Lunner T. Hearing loss impacts neural alpha oscillations under adverse listening conditions. Front Psychol 2015; 6: 177
• 26 Nasreddine ZS, Phillips NA, Bédirian V. et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005; 53 (04) 695-699
• 27 Ryan DB, Eckert MA, Sellers EW. et al. Impact of effortful word recognition on supportive neural systems measured by alpha and theta power. Ear Hear 2022; 43 (05) 1549-1562
• 28 Wilson RH. Development of a speech-in-multitalker-babble paradigm to assess word-recognition performance. J Am Acad Audiol 2003; 14 (09) 453-470
• 29 Wilson RH, Abrams HB, Pillion AL. A word-recognition task in multitalker babble using a descending presentation mode from 24 dB to 0 dB signal to babble. J Rehabil Res Dev 2003; 40 (04) 321-327
• 30 Tillman TW, Carhart R. An Expanded Test for Speech Discrimination Utilizing CNC Monosyllabic Words. Northwestern University Auditory Test No. 6. Northwestern University; Evanston, IL; Auditory Research Lab; 1966. Accessed October 1, 2020 at: https://apps.dtic.mil/docs/citations/AD0639638
• 31 Wilson RH, Watts KL. The Words-in-Noise Test (WIN), list 3: a practice list. J Am Acad Audiol 2012; 23 (02) 92-96
• 32 Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 2004; 134 (01) 9-21
• 33 Pion-Tonachini L, Kreutz-Delgado K, Makeig S. ICLabel: an automated electroencephalographic independent component classifier, dataset, and website. Neuroimage 2019; 198: 181-197
• 34 Lin Z, Tam F, Churchill NW. et al. Trail making test performance using a touch-sensitive tablet: behavioral kinematics and electroencephalography. Front Hum Neurosci 2021; 15: 663463
• 35 Obleser J, Kotz SA. Multiple brain signatures of integration in the comprehension of degraded speech. Neuroimage 2011; 55 (02) 713-723
• 36 Bernarding C, Strauss DJ, Hannemann R, Seidler H, Corona-Strauss FI. Neural correlates of listening effort related factors: influence of age and hearing impairment. Brain Res Bull 2013; 91: 21-30
• 37 Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw 2015; 67 (01) 1-48
• 38 Wöstmann M, Obleser J. Acoustic detail but not predictability of task-irrelevant speech disrupts working memory. Front Hum Neurosci 2016; 10: 538
• 39 Eckert MA, Teubner-Rhodes S, Vaden Jr KI. Is listening in noise worth it? The neurobiology of speech recognition in challenging listening conditions. Ear Hear 2016; 37, Suppl 1 (Suppl. 01) 101S-110S
• 40 Bidelman GM, Price CN, Shen D, Arnott SR, Alain C. Afferent-efferent connectivity between auditory brainstem and cortex accounts for poorer speech-in-noise comprehension in older adults. Hear Res 2019; 382: 107795
• 41 Eckert MA, Cute SL, Vaden Jr KI, Kuchinsky SE, Dubno JR. Auditory cortex signs of age-related hearing loss. J Assoc Res Otolaryngol 2012; 13 (05) 703-713
• 42 Peelle JE, Troiani V, Grossman M, Wingfield A. Hearing loss in older adults affects neural systems supporting speech comprehension. J Neurosci 2011; 31 (35) 12638-12643
• 43 Lunner T, Alickovic E, Graversen C, Ng EHN, Wendt D, Keidser G. Three new outcome measures that tap into cognitive processes required for real-life communication. Ear Hear 2020; 41, Suppl 1 (Suppl. 01) 39S-47S
• 44 Lin FR. Hearing loss and cognition among older adults in the United States. J Gerontol A Biol Sci Med Sci 2011; 66 (10) 1131-1136
• 45 Husain FT, Medina RE, Davis CW. et al. Neuroanatomical changes due to hearing loss and chronic tinnitus: a combined VBM and DTI study. Brain Res 2011; 1369: 74-88
• 46 Belkhiria C, Vergara RC, San Martín S. et al. Cingulate cortex atrophy is associated with hearing loss in presbycusis with cochlear amplifier dysfunction. Front Aging Neurosci 2019; 11: 97
• 47 Levitt H. Transformed up-down methods in psychoacoustics. J Acoust Soc Am 1971; 49 (2B): 467-477