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Building a Conceptual Framework for Auditory Learning

Kraus, Nina PhD

doi: 10.1097/01.HJ.0000470891.91317.17

Dr. Kraus (brainvolts.northwestern.edu is a professor of auditory neuroscience at Northwestern University, investigating the neurobiology underlying speech and music perception and learning-associated brain plasticity.



Here's a question: What do you get when you combine a rabbit, a violin, and a patient who is struggling to hear in noise?

You probably didn't guess “a life in auditory neuroscience,” but, when taken together, the three encapsulate my research: the biology of auditory learning.

I began my career measuring responses from single neurons in the rabbit auditory cortex. I trained rabbits to associate a particular sound with a particular meaning and found striking changes in neural activity within the auditory cortex following this learning experience.

Having witnessed the potential for brain plasticity firsthand, I moved my focus to improving human communication. Since then, I have pursued several lines of work to understand how the brain is changed by experiences such as music training,1 second language learning,2 and computerized brain training (Listening and Communication Enhancement [LACE];3 BrainHQ4).

These studies demonstrate the potential for brain plasticity, and we've defined signature neural changes stemming from each experience.5 I've covered many of these neural signatures in the Hearing Matters columns Samira Anderson, AuD, PhD, and I have written for The Hearing Journal (HJ January 2015 issue, p. 38, but, in this editorial, I want to take a step back and ask: What factors drive the biological changes?

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Effective training covers these bases:

  • Cognitive: A listener has to direct attention to sound, remember that sound, and connect that sound to meaning.
  • Sensorimotor: A listener has to learn to hear fine-grained sound details, such as recognizing the difference between “b” and “p,” and associate those sounds with precise motor actions, such as articulating those consonants.
  • Reward: Emotional systems in the brain have a chief role in sparking neuroplasticity, and this rewarding jolt makes changes last.6,7

If all these brain systems are engaged together, they have the potential to change automatic sound processing in the brain, providing a lasting benefit for listening even after training has stopped.

In my research I have sought biological metrics of automatic processing that reflect this cognitive–sensorimotor–reward trifecta, first using the mismatch negativity (MMN)8,9 and now the auditory brainstem response to complex sounds (cABR).10

Take the case of playing a musical instrument. Making music demands attention and memory while you're honing in on fine acoustic details such as timbre and pitch and carefully coordinating motor action on the instrument, all while drawing pleasure from the sounds you're creating and manipulating.

It's no coincidence, then, that comparisons of children engaged in active music making versus music appreciation classes have found brain changes only in the former.11 These themes extend past music to cut across research in auditory learning, including experiments in humans12,13 and animal models.6,14

But where does this information leave that person who's grasping at straws to hear in noise? These biological lessons in learning tell us a lot about how to help her. They teach us that simply improving the sensory component (such as amplification) may only begin to improve how the brain processes sound.

Auditory training might be a good strategy, and we now know that a promising intervention must tick three boxes: cognitive, sensorimotor, and reward.

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1. Kraus N, Slater J, Thompson EC, et al. Music enrichment programs improve the neural encoding of speech in at-risk children.J Neurosci 2014;34(36):11913-11918.
2. Krizman J, Marian V, Shook A, Skoe E, Kraus N. Subcortical encoding of sound is enhanced in bilinguals and relates to executive function advantages. Proc Natl Acad Sci U S A 2012;109(20):7877-7881.
3. Song JH, Skoe E, Banai K, Kraus N. Training to improve hearing speech in noise: biological mechanisms. Cereb Cortex 2012;22(5):1180-1190.
4. Anderson S, White-Schwoch T, Parbery-Clark A, Kraus N. Reversal of age-related neural timing delays with training. Proc Natl Acad Sci U S A 2013;110(11):4357-4362.
5. Kraus N, Nicol T. The cognitive auditory system: the role of learning in shaping the biology of the auditory system. In: Perspectives on Auditory Research.Popper AN, Fay RR, eds. Springer Handbook of Auditory Research. New York, NY: Springer Science + Business Media; 2014:299-319.
6. Bakin JS, Weinberger NM. Induction of a physiological memory in the cerebral cortex by stimulation of the nucleus basalis. Proc Natl Acad Sci U S A 1996;93(20):11219-11224.
7. Engineer ND, Riley JR, Seale JD, et al. Reversing pathological neural activity using targeted plasticity. Nature 2011;470(7332):101-104.
8. Kraus N, McGee TJ, Carrell TD, Zecker SG, Nicol TG, Koch DB. Auditory neurophysiologic responses and discrimination deficits in children with learning problems. Science 1996;273(5277):971-973.
9. Tremblay K, Kraus N, McGee T. The time course of auditory perceptual learning: neurophysiological changes during speech–sound training. NeuroReport 1998;9(16):3557-3560.
10. Skoe E, Kraus N. Auditory brainstem response to complex sounds: a tutorial. Ear Hear 2010;31(3):302-324.
11. Kraus N, Slater J, Thompson EC, et al. Auditory learning through active engagement with sound: biological impact of community music lessons in at-risk children. Front Neurosci 2014;8:351.
12. Kuhl PK. Is speech learning ‘gated’ by the social brain? Dev Sci 2007;10(1):110-120.
13. Benasich AA, Choudhury NA, Realpe-Bonilla T, Roesler CP. Plasticity in developing brain: active auditory exposure impacts prelinguistic acoustic mapping. J Neurosci 2014;34(40):13349-13363.
14. Polley DB, Steinberg EE, Merzenich MM. Perceptual learning directs auditory cortical map reorganization through top-down influences. J Neurosci 2006;26(18):4970-4982.
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